The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. Is e buidheann carthannais a th’ ann an Oilthigh Dhùn Èideann, clàraichte an Alba, àireamh clàraidh SC005336.

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The GIPSA-lab, Grenoble, is offering a

Ph.D. Position in Cognitive Neuroscience

Senses of confidence and effort in sensorimotor adaptation

(speech and reaching) 

Application deadline: 31/12/22; Starting date: 1/04/23 at the latest

 

Context

Numerous studies have explored sensorimotor learning in hand movements and speech production. They showed how individuals adapt their gestures in a way that compensates partially for the perturbation induced on the visual, auditory or somatosensory feedback. Varying degrees of compensation were observed across individuals (1–3) – in particular for pathological populations (4,5), for different sensorimotor perturbations (e.g. pitch or formant shifted feedback (6,7)), different languages (8) or different tasks (e.g. including linguistic confusion or not) (9,10).

Scientific objectives

In complement to this existing literature (7,11), the current project aims at exploring in more detail the factors influencing this varying degree of compensation to a sensorimotor perturbation. We will explore, in particular, the hypothesis that it may be influenced by the relative attention and confidence given to our different sensory feedbacks (visual and proprioceptive feedbacks for hand movements, auditory and proprioceptive feedbacks for speech), and to the related sense of effort felt in the task.

To that goal, several experiments of both visuo-motor and audio-motor perturbation will be conducted (see Figure 1). In a first behavioral step, we will explore how the degree of arm or speech compensation varies with an increasing rotation of the visual feedback or an increasing pitch shift of the auditory feedback, how it is influenced by the location or the pitch level of the target, and how it may be affected by an increasing degree of visual blurring or auditory masking. We will pay attention, in particular, to possible reorganizations of the compensatory behavior, detected from discontinuities in the compensation/perturbation relationship. 

 

Depending on the candidate’s interests and/or funding opportunities, a second step will explore further the neural correlates of these compensatory mechanisms (12–16) and of their possible re-organization with an increasing level of perturbation, using fMRI neuro-imaging; or the second step of the project will explore the possible impairment of these mechanisms in people who stutter, who demonstrate reduced degrees of compensation to an auditory perturbation (17–20), reduced tactile sensibility of the oral cavity (21–23), and increased sense of effort (24).

 

Required skills 

We are searching for a highly motivated candidate with:

- a Master degree (M.Sc., M. Eng. or equivalent) in (neuro)cognitive sciences, computer science, or signal processing

- knowledge and interest in motor control, neurosciences and speech.
- good programming skills in Matlab, Python or R

- experimental skills and interests

Lab and supervision

The PhD candidate will be supervised by Maëva Garnier, Fabien Cignetti and Pascal Perrier, in collaboration between the GIPSA-lab and TIMC-IMAG in Grenoble. He/she will join the PCMD team of GIPSA-lab in Grenoble, composed of six PhD students and 12 researchers and engineers (http://www.gipsa-lab.grenoble-inp.fr/en/pcmd.php)

Application instructions

The application consists of a motivation letter, CV (with detailed list of courses related to computer science, signal processing, and neuro-cognitive science), names and contact details of two references, and transcripts of grades from under-graduate and graduate programs.

Contact

Maëva Garnier      Email:   maeva.garnier@gipsa-lab.fr                    Phone: (+33) 4 76 57 50 61

Fabien Cignetti     Email:   fabien.cignetti@univ-grenoble-alpes.fr   Phone: (+33) 4 76 63 71 10

Pascal Perrier       Email:   pascal.perrier@gipsa-lab.fr                    Phone: (+33) 4 76 57 48 25 

 

References

1.    Ghosh SS, Matthies ML, Maas E, Hanson A, Tiede M, Ménard L, et al. An investigation of the relation between sibilant production and somatosensory and auditory acuity. J Acoust Soc Am. 2010;128(5):3079–87.

2.    Villacorta VM, Perkell JS, Guenther FH. Sensorimotor adaptation to feedback perturbations of vowel acoustics and its relation to perception. J Acoust Soc Am. 2007;122(4):2306–19.

3.    Savariaux C, Perrier P. Compensation strategies for the perturbation of the rounded vowel [u] using a lip tube: A study of the control space in speech production. J Acoust Soc Am. 1995;98(5):2428–42.

4.    Loucks T, Chon H, Han W. Audiovocal integration in adults who stutter: Audiovocal integration in stuttering. Int J Lang Commun Disord. 2012 Jul;47(4):451–6.

5.    Mollaei F, Shiller DM, Baum SR, Gracco VL. Sensorimotor control of vocal pitch and formant frequencies in Parkinson’s disease. Brain Res. 2016;1646:269–77.

6.    Jones JA, Munhall KG. Perceptual calibration of F0 production: evidence from feedback perturbation. J Acoust Soc Am. 2000 Sep;108(3 Pt 1):1246–51.

7.    MacDonald EN, Goldberg R, Munhall KG. Compensations in response to real-time formant perturbations of different magnitudes. J Acoust Soc Am. 2010 Feb 1;127(2):1059–68.

8.    Mitsuya T, MacDonald EN, Purcell DW, Munhall KG. A cross-language study of compensation in response to real-time formant perturbation. J Acoust Soc Am. 2011;130(5):2978–86.

9.    Bourguignon NJ, Baum SR, Shiller DM. Lexical-perceptual integration influences sensorimotor adaptation in speech. Front Hum Neurosci. 2014;8:208.

10.  Frank AF. Integrating linguistic, motor, and perceptual information in language production. University of Rochester; 2011.

11.  Liu H, Larson CR. Effects of perturbation magnitude and voice F0 level on the pitch-shift reflex. :8.

12.  Behroozmand R, Korzyukov O, Sattler L, Larson CR. Opposing and following vocal responses to pitch-shifted auditory feedback: Evidence for different mechanisms of voice pitch control. J Acoust Soc Am. 2012 Oct;132(4):2468–77.

13.  Parkinson AL, Flagmeier SG, Manes JL, Larson CR, Rogers B, Robin DA. Understanding the neural mechanisms involved in sensory control of voice production. Neuroimage. 2012;61(1):314–22.

14.  Toyomura A, Fujii T, Kuriki S. Effect of external auditory pacing on the neural activity of stuttering speakers. NeuroImage. 2011 Aug 15;57(4):1507–16.

15.  Zarate JM, Wood S, Zatorre RJ. Neural networks involved in voluntary and involuntary vocal pitch regulation in experienced singers. Neuropsychologia. 2010;48(2):607–18.

16.  Zarate JM, Zatorre RJ. Experience-dependent neural substrates involved in vocal pitch regulation during singing. Neuroimage. 2008;40(4):1871–87.

17.  Kim KS, Daliri A, Flanagan JR, Max L. Dissociated Development of Speech and Limb Sensorimotor Learning in Stuttering: Speech Auditory-motor Learning is Impaired in Both Children and Adults Who Stutter. Neuroscience. 2020 Dec 15;451:1–21.

18.  Daliri A, Wieland EA, Cai S, Guenther FH, Chang SE. Auditory-motor adaptation is reduced in adults who stutter but not in children who stutter. Dev Sci. 2018;21(2):e12521.

19.  Cai S, Beal DS, Ghosh SS, Tiede MK, Guenther FH, Perkell JS. Weak Responses to Auditory Feedback Perturbation during Articulation in Persons Who Stutter: Evidence for Abnormal Auditory-Motor Transformation. Larson CR, editor. PLoS ONE. 2012 Jul 23;7(7):e41830.

20.  Sengupta R, Shah S, Gore K, Loucks T, Nasir SM. Anomaly in neural phase coherence accompanies reduced sensorimotor integration in adults who stutter. Neuropsychologia. 2016;93:242–50.

21.  De Nil LF, Abbs JH. Kinaesthetic acuity of stutterers for oral and non-oral movements. Brain. 1991;114(5):2145–58.

22.  Loucks TM, De Nil LF. Oral kinesthetic deficit in adults who stutter: a target-accuracy study. J Mot Behav. 2006;38(3):238–47.

23.  Loucks TMJ, De Nil LF. Oral kinesthetic deficit in stuttering evaluated by movement accuracy and tendon vibration. Speech Mot Control Norm Disord Speech. 2001;307–10.

24.  Ingham RJ, Warner A, Byrd A, Cotton J. Speech effort measurement and stuttering: Investigating the chorus reading effect. 2006;

25.  Caudrelier T, Rochet-Capellan A. Changes in speech production in response to formant perturbations: An overview of two decades of research. 2019. 

 

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Real time speaker separation

Master internship, Lille (France), 2022

Advisors — Nathan Souviraà-Labastie, R&D Engineer, PhD, nathan.souviraa-labastie@steelseries.com — Damien Granger, R&D Engineer, damien.granger@steelseries.com

Company description About GN Group GN was founded 150 years ago with a truly innovative and global mindset. Today, we honour that legacy with world-leading expertise in the human ear, sound and video processing, wireless technology, miniaturization and collaborations with leading technology partners. GN’s solutions are marketed by the brands ReSound, Beltone, Interton, Jabra, BlueParrott, SteelSeries and FalCom in 100 countries. The GN Group employs 6,500 people and is listed on Nasdaq Copenhagen (GN.CO).

About SteelSeries SteelSeries is the worldwide leader in gaming and esports peripherals focused on premium quality, innovation, and functionality. SteelSeries’ family of professional and gaming enthusiasts are the driving force behind the company and help influence, design, and craft every single accessory and the brand’s software ecosystem, SteelSeries GG. In 2020, SteelSeries acquired Nahimic, the leader in 3D sound solutions for gaming. We are currently looking for a machine learning / audio signal processing intern to join the R&D team of SteelSeries’ Software & Services Business Unit in our French office (former Nahimic R&D team).

Internship subject Audio source separation consists in extracting the different sound sources present in an audio signal, in particular by estimating their frequency distributions and/or spatial positions. Many applications are possible from karaoke generation to speech denoising. In 2020, our separation approaches [1, 2] were equaling the state of the art [3, 4] on a music separation task. Since then our speech denoising product has hit the market [5] and the team continue to explore many tracks of improvements (see for instance the following project [6, 7]). Real time speaker separation This internship targets speaker separation which is formalized in the scientific community as the task of separately retrieving a given number of speech/speaker signals from a monaural mixture signal. Most of the scientific challenges [8] compare offline (not real-time) approaches. The objective of the internship is to address the following targets (more or less ordered) : — Based on our current speech denoising trainsets, the candidate will create a trainset for the speaker separation task that match the same in-house requirement. Indeed, most of the available datasets in the scientific community lack quantity, audio quality of the groundtruths, high sampling rate, diversity of speakers/noise type. In addition, for the SteelSeries use cases, the overlap in time of the different speech sources might be lower than in the scenarii used by the scientific community and it statistical distribution will need to be well identified/defined. — Once our offline and online baseline algorithm have been trained on such a trainset, the candidate could benchmark on different scenarii (number of speaker, signal ratio between speakers, effect of additional noise, various and mixed languages) to potentially fulfill the weakness of the trainset. — The first subjective listening could bring the candidate to design complementary metrics, for instance representing false positive in speaker attribution or representating the statistics about the time needed by real-time DNN to correctly attribute a signal to the correct speaker after some silence. 1 — While all the above could be done using state-of-the-art loss functions, the candidate could also adapt our internal loss to be permutation invariant [9]. — The scientific community is very active in proposing new DNN architectures (offline [10, 8] and online [11, 12]. The candidate could also re-implement or propose her/his own architecture. In particular, a multi-task approach where the DNN also outputs the number of active speakers would be of great interest.

Skills Who are we looking for ? Preparing an engineering degree or a master’s degree, you preferably have knowledge in the development and implementation of advanced machine learning algorithms. Digital audio signal processing skills is a plus. Whereas not mandatory, notions in the following additional various fields would be appreciated : Audio effects in general : compression, equalization, etc. - Statistics, probabilist approaches, optimization. - Programming language : Python, Pytorch, Keras, Tensorflow, Matlab. - Voice recognition, voice command. - Computer programming and development : Max/MSP, C/C++/C#. - Audio editing software : Audacity, Adobe Audition, etc. - Scientific publications and patent applications. - Fluent in English and French. - Demonstrate intellectual curiosity.

Références [1] I. Alaoui Abdellaoui et N. Souviraà-Labastie. « Blending the attention mechanism in TasNet ». working paper or preprint. Nov. 2020. [2] E. Pierson Lancaster et N. Souviraà-Labastie. « A frugal approach to music source separation ». working paper or preprint. Nov. 2020. [3] F.-R. Stöter, A. Liutkus et N. Ito. « The 2018 signal separation evaluation campaign ». In : International Conference on Latent Variable Analysis and Signal Separation. Springer. 2018, p. 293-305. [4] N. Takahashi et Y. Mitsufuji. « Multi-scale Multi-band DenseNets for Audio Source Separation ». In : 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). 29 juin 2017. arXiv : 1706.09588. [5] ClearCast AI Noise Canceling - Promotion video. https : / / www . youtube . com / watch ? v = RD4eXKEw4Lg. [6] M. Vial et N. Souviraà-Labastie. Learning rate scheduling and gradient clipping for audio source separation. Rapp. tech. SteelSeries France, déc. 2022. [7] The torchcustoml rschedulersGitHubrepository. https : / / github . com / SteelSeries / torch _ custom_lr_schedulers. [8] Speech separation task referenced on the paperswithcode website. https://paperswithcode.com/ task/speech-separation. [9] X. Liu et J. Pons. « On permutation invariant training for speech source separation ». In : ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2021, p. 6-10. [10] Music separation task referenced on the paperswithcode website. https://paperswithcode.com/ sota/music-source-separation-on-musdb18. [11] DNS challenge on the paperswithcode website. https://paperswithcode.com/sota/speechenhancement-on-deep-noise-suppression. [12] H. Dubey et al. « Icassp 2022 deep noise suppression challenge ». In : ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2022, p. 9271-9275

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Creation of a speech synthesis model from spontaneous speech

Keywords:

Speech synthesis, spontaneous speech, low ressource languages, Nigerian Pidgin Context Nigerian Pidgin is a large but under-resourced language that increasingly serves as the primary vernacular language of Africa’s most populous country. Once stigmatized as a “broken” variety of English spoken only by the uneducated, Nigerian Pidgin is now a source of pride for many speakers who view it as a home-grown vehicle for communication. It transcends class and ethnicity, lacking the tribal associations of indigenous languages and the colonial baggage associated with English. The language can now be seen and heard in college campuses, houses of worship, advertisements, Nigerian expat communities, and even on a local branch of the British Broadcasting Channel.

Objectives

Despite Nigerian Pidgin’s growing prestige and a pool of speakers rivaling those of major languages like Turkish or Korean, the grammatical and intonational properties of the language are comparatively understudied. This internship is the extension of an ongoing research project aimed at better understanding its linguistic properties through the development and adaptation of NLP technologies. This research’s principal aim is to produce a natural-sounding text-tospeech (TTS) model that will allow researchers to conduct perception tests to determine how intonation influences the interpretation of meaning. Thanks partly to the recent explosion of neural network-based speech technologies, researchers can now produce high-quality synthesis from relatively simple datasets using models like TacoTron 2, complementing classical approaches such as those based on Hidden Markov Models. Specifically, the intern will assist in developing a text-to-speech platform trained on an existing database of Nigerian Pidgin recordings. In addition to producing natural-sounding speech, a central goal of this project will be to build a TTS model that will allow for the direct modification of intonational patterns via explicit parameters provided by researchers. The intern’s work will contribute to the exploration of the language’s melodic and tonal properties by allowing researchers to produce variations of novel utterances differing only by their intonational patterns.

Primary tasks

• Surveying existing TTS models and selecting the most suitable approach

• Training a model on a corpus of Nigerian Pidgin • Optimizing and evaluating the model

Profile

A second-year master’s student with:

• A solid background in machine learning (speech synthesis is a plus)

• Good academic writing skills in English

• An strong interest in language and linguistics

Sous la tutelle de : www.lisn.upsaclay.fr | Twitter @LisnLab | LinkedIn LisnLab Site Belvédère : Campus Universitaire Bâtiment 507 Rue du Belvédère – 91405 Orsay Cedex Site Plaine : Campus Universitaire bâtiment 650 Rue Raimond Castaing – 91190 Gif-sur-Yvette M2-CS-Intenship 2022-2023

Modalities

The internship will take place from March 2023 for 5 to 6 months at the LISN lab in the Sciences and Language Technologies department, as well as in the MoDyCo lab at Paris Nanterre University (primarily at the location of shortest commute).

• The LISN’s Belvédère site is located in the plateau de Saclay: University campus building 507, rue du Belvédère, 91400 Orsay.

• The MoDyCo lab is located at the Université Paris Ouest Nanterre La Défense: Bâtiment A, 200, avenue de la République – 92001 Nanterre. The candidate will be supervised by Emmett Strickland (MoDyCo) and Marc Evrard (LISN). Allowance under the official standards (service-public.fr).

To apply

Please send a CV and brief cover letter highlighting your interest in the project to the following: • Emmett Strickland (emmett.strickland@parisnanterre.fr) • Marc Evrard (marc.evrard@lisn.upsaclay.fr)

Further reading

1. Tan, X., Qin, T., Soong, F., & Liu, T. Y. (2021). A survey on neural speech synthesis. arXiv preprint arXiv:2106.15561. https://arxiv.org/abs/2106.15561

2. Ning, Y., He, S., Wu, Z., Xing, C., & Zhang, L. J. (2019). A Review of Deep Learning Based Speech Synthesis. Applied Sciences (2076-3417), 9(19). https://www.mdpi.com/2076- 3417/9/19/4050

3.Bigi, B., Caron, B., & Abiola, O. S. (2017). Developing resources for automated speech processing of the african language naija (nigerian pidgin). In 8th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics (pp. 441-445). https://hal.archives-ouvertes.fr/hal-01705707/document

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Two full-time Postdoctoral Research Fellows in automatic lyrics generation and automatic singing voice/speech evaluation.

You can find the detailed job descriptions here:

https://smcnus.comp.nus.edu.sg/postdoct_job_description_2022

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FORMULAIRE DE STAGE RECHERCHE Intitulé du  projet CAIBots: Conversational AI with teams of robots

Encadrants Prof. Fabrice Lefèvre

Descriptif du stage : L'objectif du stage consiste à étudier la mise en place d’un dispositif robotique permettant la simulation en « conditions réelles » des IA conversationnelles (CAI) vocales. Entraîner puis tester de l’IA conversationnelle (chatbots, systèmes de dialogue) est couteux et complexe, nous souhaitons grandement réduire cette difficulté en fournissant une solution robotique physique autonome pour apprendre et évaluer de nouveaux modules pour la CAI avant de les utiliser avec de vrais utilisateurs humains. Dans un premier temps, il s’agira principalement de tester des solutions existantes et clefs en main pour les éléments de la chaîne de traitement du langage parlé et de vérifier leur niveau de performance en configuration robot-robot. Ensuite une recherche vers des solutions embarquées sera menée. Elle devra permettre d’améliorer la latence du dispositif mais aussi d’assurer une meilleure protection des données personnelles (en ôtant la nécessité du passage par des clouds propriétaires). Globalement le système d'interaction vocal mis en place devra permettant une discussion ouverte entre un humain et une machine sur des sujets généraux. Le cas d’usage envisagé se positionne donc dans la logique du challenge Amazon Alexa (https://developer.amazon.com/alexaprize) : développer un bot pouvant entretenir une conversation pendant quelques minutes. Il sera donc nécessaire de prévoir aussi un utilisateur simulé pour permettre une interaction robot-robot autonome (le cas de conversations multiparties humains-robots pourra aussi être testé, sans être un objectif prioritaire du stage). Il s'agira d’initier le dispositif, c'est à dire de mettre en place les composants en configuration de base, mais illustrant les capacités potentielles pouvant être atteintes avec un temps de développement plus conséquent. Les solutions robotiques et logicielles entrevues pour ce travail sont, par exemple : robot Pepper, Google Cloud ASR, SpeechBrain, RASA et/ou des modèles pré-entraînés (BERT, GPT, BlenderBot…) ... Il s’agit principalement de plateformes open-source, assez complètes. Le travail consistera à mettre en œuvre rapidement un système réel afin de pouvoir le faire progresser en configuration robot-robot puis le tester avec un panel représentatif d'utilisateurs potentiels. Si un intérêt pour l'apprentissage automatique et le traitement de la langue naturelle est essentiel, il est aussi attendu du stagiaire de bonnes capacités en développement logiciel. Le stage sera une occasion d'acquérir des compétences en traitement automatique de la langue dans un contexte d'expérimentation en robotique embarquée. Plusieurs pistes pour une prolongation en thèse sont ouvertes. Durée du stage 6 mois Rémunération Environ 540€ / mois Thématique associée au stage Systèmes de dialogue humain-machine, reconnaissance et compréhension de parole, interface cognitive, robotique

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Sujet de stage de M2 : Décodage des signaux EEG à l’aide des méthodes d’apprentissage automatique

Contexte L’EEG (électroencéphalographie) est une technique non invasive, qui permet de mesurer l’activité électrique du cerveau à l’aide d’électrodes placées sur la tête. Ces électrodes enregistrent l’activité électrique causées par les neurones. Les données recueillies sont enregistrées et peuvent être, à l’aide des méthodes d’apprentissage automatique, utilisées à diverses fins comme par exemple : analyse, classification ou interface neuronale directe [Cao20]. Dans le cadre du traitement automatique du langage et de la parole, des premiers travaux, avec des résultats préliminaires, sont apparus récemment (e.g. classification EEG avec une approche par Transformer [Sun21], des données EEG issues de la lecture de phrases [Hollenstein18] ou encore dans le cadre de méthodes combinant l’utilisation d’EEG avec des techniques de traitement du langage pour la détection de préférences utilisateur [Gauba17]).

Objectif L’objectif de ce stage consiste à reconnaître automatiquement des caractères dans un premier temps, puis des mots isolés, énoncés oralement via les signaux EEG. Les différentes étapes du stage peuvent se résumer comme suit : 1. Prise en main du casque EEG Emotiv EPOC-X (https://www.emotiv.com/epoc-x/). 2. Mettre en place un protocole expérimental et collecter un corpus permettant la mise en place des expériences. 3. Evaluer et choisir des algorithmes d’apprentissage automatique pour reconnaitre les caractères et/ou mots isolés à partir des signaux EEG du corpus collecté dans le cadre du stage.

Profil du candidat L’étudiant.e doit être en dernière année de diplôme d’ingénieur ou en Master 2 d’informatique. Il ou elle doit posséder des notions de programmation, maîtriser l’environnement Linux et les méthodes standard d’apprentissage automatique. Durée du stage 6 mois de février à mars 2023.

Lieu du stage Le stage aura lieu au sein du Laboratoire Informatique d’Avignon (LIA) à Avignon Université ou au sein du Laboratoire des Sciences du Numérique de Nantes (LS2N) à l’Université de Nantes.

Gratification Le stage sera gratifié selon le montant horaire en vigueur au 01/01/2023, considérant une convention de stage de 35 heures par semaine (4,05 euros soit ≃550 €/mois).

Comment postuler ? Merci d’envoyer par email à Mickael Rouvier (mickael.rouvier@univavignon.fr) et Richard Dufour (richard.dufour@univ-nantes.fr) les documents suivants : 1) CV ; 2) relevé de notes (licence et master) et 3) lettre de motivation.

Bibliographie

[Cao20] Cao, Z. (2020). A review of artificial intelligence for EEG-based brain− computer interfaces and applications. Brain Science Advances, 6(3), 162-170.

[Gauba17] Gauba, H., Kumar, P., Roy, P. P., Singh, P., Dogra, D. P., & Raman, B. (2017). Prediction of advertisement preference by fusing EEG response and sentiment analysis. Neural Networks, 92, 77-88.

[Hollenstein18] Hollenstein, N., Rotsztejn, J., Troendle, M., Pedroni, A., Zhang, C., & Langer, N. (2018). ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading. Scientific data, 5(1), 1-13.

[Sun21] Sun, J., Xie, J., & Zhou, H. (2021). EEG classification with transformer-based models. In 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech) (pp. 92-93).IEEE

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Ref 6581/22, Postdoctoral Research Fellow, MARCS Institute for Brain, Behaviour & Development

About Western

Western Sydney University is a modern, forward-thinking, research-led university, located at the heart of Australia’s fastest-growing and economically significant region, Western Sydney. Boasting 11 campuses – many in Western Sydney CBD locations – and more than 200,000 alumni, 49,500 students and 3,500 staff, the University has 14 Schools with an array of well-designed programs and degrees carefully structured to meet the demands of future industry. The University is ranked in the top two per cent of universities worldwide, and as a research leader, over 85 per cent of the University’s assessed research is rated at ‘World Standard’ or above.

About the Role The MARCS

Institute for Brain, Behaviour and Development is seeking to appoint a Postdoctoral Research Fellow to join the Brain Sciences research program at Western Sydney University. This two year Postdoctoral Research Fellow position is funded by an ARC Discovery grant, “Investigating the characteristics of older adults' conversation behaviour” awarded to Chief Investigators (CIs) Professor Chris Davis and Professor Jeesun Kim at the MARCS Institute for Brain, Behaviour and Development, Western Sydney University, in collaboration with Partner Investigators (PI) Emeritus Professor Valerie Hazan at University College London. This project will investigate the production and perception of naturalistic conversations by young and older adults. In particular, the interest is in probing individualised semantic processing. The aim of the project is to understand factors that affect the engagement of older adults in conversations. In this role you will work with the team to develop, pilot, and apply new methods for probing individual-specific semantic processing and on the collection of sensory, perceptual, cognitive and electrophysiological (EEG) data. You will also implement procedures for analysing the data. The successful applicant will work closely with the above investigators and the rest of the research team, conducting studies and participating in supervising and/or training PhD students, Research Assistants, Honours students, and interns enlisted into the project. For further information about the role please refer to the attached Position

Description.

This is a full time, 2 year fixed term contract. Located in the new Westmead Innovation Quarter (WIQ) – a visionary research, health, education and business hub located in the Westmead Health Precinct. About MARCS The MARCS Institute for Brain, Behaviour and Development is an interdisciplinary research institute of Western Sydney. The vision for the Institute is to optimise human interaction and wellbeing across the lifespan. To strive to solve the problems that matter most through the themes: sensing and perceiving, interacting with each other, and technologies for humans. Researchers in MARCS come from many disciplines including cognitive science, developmental psychology, language science, music science, cognitive neuroscience, and biomedical, electrical, electronic and software engineering. Further information is available from our website - http://www.westernsydney.edu.au/marcs

About You

You will hold a relevant doctoral qualification, or substantial progress toward a PhD in Psychology, Cognitive Neuroscience, Computational linguistics or a related discipline. You must have experience in conducting neuroimaging and behavioural experiments.

Culture

Western Sydney University highly values equity and inclusiveness. We have a proud history of doing so and consider this an important part of our social and civic responsibilities as a University. We strive to contribute to tackling inequalities and promoting wellbeing within our own institution, the Greater Western Sydney region, nationally and internationally.

Remuneration Package:

Academic Level A: $103,304 to $124,808 p.a. (comprising Salary of $87,293 to $105,464 p.a., plus Superannuation and Leave Loading) Academic Level B: $131,136 to $154,738 p.a. (comprising Salary of $110,811 to $130,846 p.a., plus Superannuation and Leave Loading) Position Enquiries: Please contact Professor Chris Davis via email at chris.davis@westernsydney.edu.au

Closing Date:

8:30pm AEDT, Sunday, 12 February 2023

Immigration Sponsorship: Employer Visa sponsorship will be provided if required. Click here to view Position Description How to Apply: · Start your application by clicking the 'begin' button. · Login to an existing account or reset your password · Preview Application Form Western Sydney University is committed to diversity and social inclusion. Applications from people of culturally and linguistically diverse backgrounds; equity target groups including women, people with disabilities, people who identify as LGBTIQ, and people of Aboriginal and Torres Strait Islander descent are encouraged.

Professor Chris Davis, PhD

The MARCS Institute for Brain, Behaviour and Development

Western Sydney University

Westmead Innovation Quarter

Building U, Level 4

160 Hawkesbury Road (Corner of Farmhouse Road)

Westmead NSW 2145

 <chris.davis@westernsydney.edu.au>

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Senior Postdoctoral Position in SLP

University of Cambridge, Department of Engineering (UK)

The ALTA Institute is looking for a senior postdoctoral researcher in spoken language processing to join our research team investigating L2 English speaking automated assessment and learning.

Website: https://www.jobs.cam.ac.uk/job/39114/

Postdoctoral Position in SLP

University of Cambridge, Department of Engineering (UK)

The ALTA Institute is looking for a postdoctoral researcher in spoken language processing to join our research team investigating L2 English speaking automated assessment and learning.

Website:  https://www.jobs.cam.ac.uk/job/39023/

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Creation of a speech synthesis model from spontaneous speech

Keywords: Machine learning, speech synthesis, low resource languages, Nigerian Pidgin

Objectives The main aim is to produce a natural-sounding text-to-speech (TTS) model allowing to perform perceptual tests for experimental linguistics. Thanks partly to the recent evolution of neural network-based speech technologies, researchers can now produce high-quality synthesis from relatively simple datasets using models like TacoTron 2, complementing classical approaches such as those based on Hidden Markov Models. Specifically, the intern will assist in developing a text-to-speech platform trained on an existing database of Nigerian Pidgin recordings. In addition to producing natural-sounding speech, a central goal of this project will be to build a TTS model that will allow for the direct modification of intonational patterns via explicit parameters provided by researchers. The intern’s work will contribute to the exploration of the language’s melodic and tonal properties by allowing researchers to produce variations of novel utterances differing only by their intonational patterns. Context This work is part of a larger project to study Nigerian Pidgin. It is a large but under-resourced language that increasingly serves as the primary vernacular language of Africa’s most populous country. Once stigmatized as a “broken” variety of English spoken only by the uneducated, Nigerian Pidgin is now a source of pride for many speakers who view it as a home-grown vehicle for communication. It transcends class and ethnicity, lacking the tribal associations of indigenous languages and the colonial baggage associated with English. The language can now be seen and heard in college campuses, houses of worship, advertisements, Nigerian expat communities, and even on a local branch of the BBC.

Primary tasks

• Surveying existing TTS models and selecting the most suitable approach

• Training a model on a corpus of Nigerian Pidgin

• Optimizing and evaluating the model Profile

A second-year master’s student with:

• A solid background in machine learning (speech synthesis is a plus)

• Good academic writing skills in English • An strong interest in language and linguistics

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Nantes Université ouvre un poste de maître de conférences en informatique pour septembre 2023. L'enseignement sera effectué au sein de la Faculté des Langues et Cultures Etrangères (FLCE) et la recherche sera menée au sein du LS2N (Laboratoire des Sciences du numérique de Nantes).

Plusieurs profils de recherche sont possibles, dont celui du traitement automatique de données langagières, pour une intégration dans l'équipe TALN (Traitement Automatique du Langage Naturel).
Plus d'informations sur l'équipe sont disponibles en ligne (http://taln.ls2n.fr).

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Technische Universität Darmstadt is one of Germany’s leading technical universities with broad excellence in research, an interdisciplinary profile, and an explicit focus on engineering sciences.

The department of Electrical Engineering and Information Technology invites applications for a

Full Professorship (W3) or Assistant Professorship (W2 with Tenure Track) for Signal Theory and Statistical Learning (Code No 6)

We are looking for an excellent researcher with credentials in at least one of the following areas: •Theory and methods of statistical inference •Robust statistical signal processing • Statistical learning theory •Theoretical performance analysis and interpretability of signal processing methods •Guarantees and interpretability of statistical learning methods •New trends in statistical signal and learning theory Application deadline: March 15, 2023 All further information about the position and application process can be found under:

https://www.tu-darmstadt.de/universitaet/karriere_an_der_tu/stellenangebote/aktuelle_stellenangebote/stellenausschreibungen_detailansichten_1_502208.en.jsp

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L'Université Paris-Saclay recrute un·e Maître·sse de Conférences en informatique (27ème section) pour la rentrée 2023. La personne recrutée travaillera au LISN, le Laboratoire Interdisciplinaire des Sciences du Numériques de l'Université Paris-Saclay, dans le département Science et Technologies des Langues (STL). Le profil recherche porte sur le traitement la langue multimodale. L'enseignement se fera à l'UFR de Sciences.

N'hésitez pas à me contacter ou à contacter un membre du département Science et Technologies des Langues si vous avez des questions. Vous trouverez ci-dessous des détails sur les profils recherche et enseignement.

Cordialement,

Gilles Adda

-- 

|Depuis le 1er janvier 2021, le LIMSI a fusionné avec le LRI et est devenu le LISN (Laboratoire Interdisciplinaire des Sciences du Numérique)
|Since January 1st 2021, LIMSI merged with the LRI lab and became the LISN (Interdisciplinary Computer Science Laboratory)
|
- 
|
|Gilles Adda
|  responsable du département Sciences et Technologies des Langues
|  head of Language Science and Technology Department
|  http://www.limsi.fr/Individu/gadda/
|

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Stockholm

Ref. No. SU FV-0793-23

Closing date: 15/04/2023

URL to this page
https://www.su.se/english/about-the-university/work-at-su/available-jobs/phd-student-positions-1.507588?rmpage=job&rmjob=20262&rmlang=UK

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We have  currently 2 open postdoc position at the LISN (ex-LIMS). You can apply online
and get more details here:
- multilingual ASR
https://emploi.cnrs.fr/Offres/CDD/UMR9015-LUCOND-003/Default.aspx?lang=EN
- ASR for low computational resource environment
https://emploi.cnrs.fr/Offres/CDD/UMR9015-LUCOND-002/Default.aspx?lang=EN

Feel free to contact me if you have any questions.

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Le service Plateformes du laboratoire GIPSA-LAB (CNRS/G-INP/UGA, Grenoble) recrute un.e ingénieur.e d?étude en instrumentation.
Il/Elle interviendra en support pour les plateformes expérimentales du laboratoire, et en particulier celles en lien avec l'aéro-acoustique, l'automatique ou la robotique. Il/elle aura notamment en charge  la responsabilité technique de la plateforme AERO, dédiée à l'étude des phénomènes aéro-acoustiques de la production de parole : réalisation de l'interfaçage d'instrumentations, gestion et entretien du parc d'appareils de mesures, préparation et participation aux expériences scientifiques.

Pour plus d'information, la fiche de poste détaillée est disponible ici : https://filesender.renater.fr/?s=download&token=2ae39938-121d-44c4-b6ce-385029ecf331

N'hésitez pas à me contacter si besoin d'information complémentaire.

Coriandre VILAIN

-- 
Coriandre Vilain
Ingénieur de Recherche UGA
Equipe PCMD, Pôle Parole & Cognition, GIPSA-LAB 
--
Responsable Service Plateformes GIPSA
--
04 76 82 77 80
www.gipsa-lab.fr/~coriandre.vilain

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Dear All,

Telecom SudParis welcomes applications for a permanent position of Associate-Assistant Professor in artificial intelligence for semantic and multi-modal multimedia analysis. Telecom SudParis is a public graduate school for engineering, which has been recognized on the highest level in the domain of digital technology. Telecom SudParis is co-founder member of the Institut Polytechnique de Paris and part of the Institut Mines-Telecom, the number one group of engineering schools in France. The recruited assistant/associate professor will join the ARTEMIS (Advanced Research and TEchniques for Multidimensional Imaging Systems) Department of Télécom SudParis and the SAMOVAR laboratory. The targeted research theme concerns the field of artificial intelligence, applied to the semantic analysis of massive, distributed and heterogeneous multimedia data. This concerns the automatic, multi-modal interpretation of complex audio-visual documents, computer vision, multimedia indexation, knowledge extraction and machine learning methodologies. The expected contributions will focus on deep neural network learning methods and target the entire multimedia content processing chain. Detailed information can be found at the following URL: https://institutminestelecom.recruitee.com/l/en/o/maitre-de-conferences-en-intelligence-artificielle-pour-lanalyse-semantique-de-donnees-multimedia-cdi The application deadline is March 31, 2023. Please do not hesitate to contact me for any further information.

Best regards,

Titus ZAHARIA, Professor

Head of the ARTEMIS Department Télécom SudParis

Institut Polytechnique de Paris

titus.zaharia@telecom-sudparis.eu

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Title of Project: Deep Cascaded Representation Learning for Speech Modelling

Supervisor:Professor Thomas Hain

Deadline for Applications:13th April 2023

The LivePerson Centre for Speech and Language offers a 3 year fully funded PhD studentship
covering standard maintenance, fees and travel support, to work on cascaded deep learning structures
to model speech. The Centre is connected with the Speech and Hearing (SpandH) and the Natural
Language Processing (NLP) research groups in the Department of Computer Science at the University
of Sheffield.
Auto-encoding is a powerful concept that allows us to compress signals and find essential
representations.Th econcept was expanded to include context, which is usually referred to as
self-supervised learning. On very large amounts of speech data this has led to very successful
methods and models for representing speech data, for a wide range of downstream processes.
Examples of such models are Wave2Vec or WaveLM. Use of their representations often requires
fine-tuning to a specific task, with small amounts of data. When encoding speech, it is desirable to
represent a range of attributes at different temporal specificity. Such attributes often reflect a hierarchy
of information.
The aim in this PhD project is to explore the use of knowledge about natural hierarchies in speech in
cascaded auto- and contextual encoder/decoder models. The objective is to describe a structured way
to understand such hierarchies. The successful candidate is expected to propose methods to combine
different kinds of supervision (auto, context, label) and build hierarchies of embeddings extractions.
These propositions may have to be seen in the context of data availability and complexity. All
proposals are to be implemented and tested on speech data. Experiments should be conducted on a
range of speech data sets with different speech types and data set size.
The student will join a world-leading team of researchers in speech and language technology. The
LivePerson Centre for Speech and Language Technology was established in 2017 with the aim to
conduct research into novel methods for speech recognition and general speech processing, including
end to end modelling, direct waveform modelling and new approaches to modelling of acoustics and
language. It has recently extended its research remit to spoken and written dialogue. The Centre hosts
severa lResearch Associates, PhD researchers,graduate and undergraduate project students,
Researchers and Engineers from LivePerson, and academic visitors. Being fully connected with
SpandH brings collaboration, and access to a wide range of academic research and opportunities for
collaboration inside and outside of the University. The Centre has access to extensive dedicated
computing resources (GPU, large storage) and local storage of over 60TB of raw speech data.

The successful applicant will work under the supervision of Prof. Hain who is the Director of the
LivePerson Centre and also Head of the SpandH research group. SpandH was and is involved in a
large number of national and international projects funded by national bodies and EU sources as well
as industry. Prof. Hain also leads the UKRI Centre for Doctoral Training In Speech and Language
Technologies and their Applications (https://slt-cdt.ac.uk/) - a collaboration between the NLP research
group and SpandH. Jointly, NLP and SpandH host more than 110 active researchers in these fields.
This project will start as soon as possible.
If English is not your first language, you must have an IELTS score of 6.5 overall, with no less than 6.0
in each component.

How to Apply:All applications must be made directly to the University of Sheffield using the
Postgraduate Online Application Form.
Information on what documents are required and a link to the application form can
be found here - https://www.sheffield.ac.uk/postgraduate/phd/apply/applying
On your application, please name Prof. Thomas Hain as your proposed supervisor
and include the title of the studentship you wish to apply for.
Your research proposal should:
●Be no longer than 4 A4 pages, including references
●Outline your reasons for applying for this studentship
●Explain how you would approach the research, including details of your
skills and experience in the topic area
If you have any queries, please contact phd-compsci@sheffield.ac.uk

Funding Details:
This position is fully funded by LivePerson, covering all tuition fees and a stipend at
the standard UKRI rate

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Job Title:Research Associate in Integrated Multitask Neural Speech Labelling
For further information and the link to apply please visit:
https://www.jobs.ac.uk/job/CXH168/research-associate-in-integrated-multitask-neural-speech-
labelling
Deadline for
Applications:15th March 2023
We are seeking an outstanding Research Associate in Integrated Multitask Neural Speech
Labelling, to join the LivePerson Centre for Speech and Language Technology based at the
University of Sheffield, which is linked with the Speech and Hearing (SpandH) research group
in the Department of Computer Science.
Youare applying to join a world-leading team of researchers in speech and language
technology to work on new ways to integrate a variety of speech technology labelling,
clustering or segmentatio ntasks into a single algorithm or process, in the context o fdeep
neural networks.Even end to end(E2E) automatic speech recognition is  typically
considered as a standalone process, independent of other speech audio technology tasks
such as diarisation, acoustic event detection or intent recognition.
The LivePerson Centre for Speech and Language Technology was established in 2017 with
the aim of conducting research into novel methods for speech recognition and general speech
processing, including end to end modelling, direct waveform modelling and new approaches
to modelling of acoustics and language. It has recently extended its research remit to spoken
and written dialogue. The Centre hosts several Research Associates, PhD researchers,
graduate and undergraduate project students, Researchers and Engineers from LivePerson,
and academic visitors, which will provide avibrant work environmentwithin the University.
Being fully connected with SpandH brings collaboration, and access to a wide range of
academic research and opportunities for collaboration inside and outside of the University.
Thepostholder wil lwork closely with Prof.Thomas Hain who is the Director of the
LivePerson Centre and also Head of the SpandH group. Prof. Hain also leads the UKRI
Centre for Doctora lTraining InSpeech and LanguageTechnologies and their
Applications(slt-cdt.ac.uk) - a collaboration between the Natural Language Processing
(NLP) research group and SpandH. Jointly, NLP and SpandH host more than 110 active
researchers in these fields.

We’re one of the best not-for-profit organisations to work for in the UK. The University’s Total
Reward Package includes a competitive salary, a generous Pension Scheme and annual
leave entitlement, as well as access to a range of learning and development courses to
support your personal and professional development.
We build teams of people from different heritages and lifestyles from across the world, whose
talent and contributions complement each other to the greatest effect. We believe diversity in
all its forms delivers greater impact through research, teaching and student experience.
To find out what makes the University of Sheffield a remarkable place to work, watch this short
film: www.youtube.com/watch?v=7LblLk18zmo, and follow @sheffielduni and @ShefUniJobs
on Twitter for more information.

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Title of Project: PhD Position in Adaptive Deep Learning for Speech and Language

Supervisor:Professor Thomas Hain
Deadline for
Applications:13th April 2023
The LivePerson Centre for Speech and Language offers a 3 year fully funded PhD studentship
covering standard maintenance, fees and travel support, to work on deep neural network adaptive
learning modules for speech and language. The Centre is connected with the Speech and Hearing
(SpandH) and the Natural Language Processing(NLP) researchg roups at the Department  of
Computer Science at the University of Sheffield.
Domain mismatch remains a key issue for speech and language technologies for which traditional
solutions are transfer learning and adaptation. The latter was widely used for modelling of speech in
the context of generative models, however less so with modern neural network approaches. Such
adaptation targeted features or models and was often informed by previous model output and
estimates of latent factors. These approaches were often informed by observations on human abilities
to adapt and adjust to new acoustic or semantic situations. Adaptation in neural networks is model
based and often implicit - through attention or dynamic convolution. However, these methods to date
still fail to reproduce the rapid learning and adaptation that humans exhibit when being exposed to new
contexts.
The objective in this project is to conduct research into neural network structures that are capable of
rapidly adjusting to a change in latent factors and at the same time allow for robust control. This will
require rapid feedback mechanisms on the mismatch between the observed data and the model
expectation. A range of strategies may be applied - through instantaneous feedback or through control
of transformational model parameters. All proposals are to be implemented and tested on speech, and
where suitable, also language data. Experiments should be conducted on a range of tasks of different
complexity in the context of different data types.
The student will join a world-leading team of researchers in speech and language technology. The
LivePerson Centre for Speech and Language Technology was established in 2017 with the aim to
conduct research into novel methods for speech recognition and general speech processing, including
end-to-end modelling, direct waveform modelling and new approaches to modelling of acoustics and
language. It has recently extended its research remit to spoken and written dialogue. The Centre hosts
severalResearchAssociates,PhDresearchers,graduateandundergraduateprojectstudents,
ResearchersandEngineers from LivePerson, and academic visitors. Being fully connected with
SpandH brings collaboration, and access to a wide range of academic research and opportunities for
collaboration inside and outside of the University. The Centre has access to extensive dedicated
computing resources (GPU, large storage) and local storage of over 60TB of raw speech data

The successful applicant will work under the supervision of Prof. Hain who is the Director of the
LivePerson Centre and also Head of the SpandH research group. SpandH was and is involved in a
large number of national and international projects funded by national bodies and EU sources as well
as industry. Prof. Hain also leads the UKRI Centre for Doctoral Training In Speech and Language
Technologies and their Applications (https://slt-cdt.ac.uk/) - a collaboration between the NLP research
group and SpandH. Jointly, NLP and SpandH host more than 110 active researchers in these fields.
This project will start as soon as possible.
If English is not your first language, you must have an IELTS score of 6.5 overall, with no less than 6.0
in each component.
How to Apply:All applications must be made directly to the University of Sheffield using the
Postgraduate Online Application Form.
Information on what documents are required and a link to the application form can
be found here - https://www.sheffield.ac.uk/postgraduate/phd/apply/applying
On your application, please name Prof. Thomas Hain as your proposed supervisor
and include the title of the studentship you wish to apply for.
Your research proposal should:
●Be no longer than 4 A4 pages, including references
●Outline your reasons for applying for this studentship
●Explain how you would approach the research, including details of your
skills and experience in the topic area
If you have any queries, please contact phd-compsci@sheffield.ac.uk
Funding
Details:
This position is fully funded by LivePerson, covering all tuition fees and a stipend at
the standard UKRI rate.

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Deux postes de MCF en phonétique sont ouverts au concours à l'Université Paul-Valéry Montpellier 3 cette année :

Phonétique générale et traitement outillé de l'oral (UMR 5267 Praxiling) : https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/ListesPostesPublies/ANTEE/2023_1/0341089Z/FOPC_0341089Z_4352.pdf

Phonétique et Didactique de l'oral en FLE (acquisition / appropriation des langues) : https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/ListesPostesPublies/ANTEE/2023_1/0341089Z/FOPC_0341089Z_4353.pdf

Date limite : 30 mars 2023

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Stockholm

Ref. No. SU FV-0793-23

Closing date: 15/04/2023

URL to this page
https://www.su.se/english/about-the-university/work-at-su/available-jobs/phd-student-positions-1.507588?rmpage=job&rmjob=20262&rmlang=UK

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The Department of Linguistics at Stockholm University invites applications for a PhD student position in experimental phonetics, including (but not limited to) topics in prosody. For details, see: https://www.su.se/english/about-the-university/work-at-su/available-jobs/phd-student-positions-1.507588?rmpage=job&rmjob=20262&rmlang=UK .

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The Digital Linguistics Lab at Bielefeld University (head: JProf. Dr.-Ing. Hendrik Buschmeier) is seeking to fill a research position (PhD-student, E13 TV-L, 100%, fixed-term) in the area of multimodal human-robot interaction in the research project ?Hybrid Living?.

Join us to work in an interdisciplinary team on research questions in the intersection of human-robot interaction and computational linguistics. Specifically, you will work (1) on the use of multimodal communication (verbal and nonverbal) to situatively instruct a service robot, (2) on making the robot's behaviour transparent to its users, and (3) on models for solving human-robot interaction problems through communication.

The formal job advertisement, with information on how to apply, can be found here: https://uni-bielefeld.hr4you.org/job/view/2265/research-position-in-multimodal-human-robot-interaction?page_lang=en

Questions? Don't hesitate to get in touch: hbuschme@uni-bielefeld.de

Hendrik Buschmeier

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We are seeking a multimodal designer to take on a central role in the Shared Reality Lab?s open source IMAGE project (image.a11y.mcgill.ca), focused on making photos, charts, and maps available to people who are blind or low vision. We are currently operating under two grants, focused on integrating haptic force feedback and pin array devices into IMAGE. You will work with a multidisciplinary team of user experience researchers, designers, and developers who will support you in designing and releasing multimodal audio and haptic experiences that will delight our end users. The primary requirement is a strong background and passion for owning both design and iterative testing of combined audio/haptic end-user experiences. Since the goal of IMAGE is to release a practical solution that can be used on a daily basis, the candidate will work directly with developers and the rest of the team to make sure that ideas and designs get translated into implementable requirements, then deployed into production.

Requirements:

• Demonstrated experience designing and creating multimodal experiences, including audio and haptics. Please include portfolio, or links to projects.
• Ability to work with user experience researchers to clearly define user needs and specific implementation requirements to meet them.
• Comfortable working directly with developers to make sure the experiences are implementable within the project timeline and available resources.
• Prototyping experience.
• Designing and carrying out methodologically sound user testing in consultation with our existing user studies lead.
• Strong plus: Sensitization to development issues, software release cycles, software architecture, client-server tradeoffs, audio synthesis.
• Bonus: French, spoken and/or written.
• Bonus: Experience working with the blind/low vision community.

Other useful skills (not required). If the candidate has the desire and capability, they are also welcome to participate in software architecture and implementation, for example:

• Audio synthesis experience. (IMAGE currently uses SuperCollider)
• Software architecture, design, and documentation, including creating usable solutions for developers and content creators in a client/server model.
• Mentoring junior designers/developers.

Candidates applying as a research assistant must be eligible to work in Canada.

Those applying as postdoctoral candidates should refer to McGill?s requirements on postdoctoral appointments, www.mcgill.ca/gps/postdocs, for conditions and additional information on the status of the position. (In Quebec, a Postdoctoral Fellow is a full-time student status and trainee category, and the Ministère de l'Education, Enseignement Supérieur et Recherche (MESRS) stipulates that all postdocs must be registered on a university student registration system.) Please note that non-Canadian postdoctoral fellows must obtain valid Citizenship or an Immigration Canada (CIC) work permit to legally work in Canada.

To apply for the position, please email the following items to Jeremy Cooperstock (srl-jobs@cim.mcgill.ca):

• A brief letter of application, describing your qualifications and relevant experience to the position of interest, along with your dates of availability.
• Detailed CV with links to online papers and/or project portfolios.
• Two (2) reference letters (sent separately).

The position is available immediately, with an initial appointment of up to one year. Informal inquiries are welcome.

About us: The Shared Reality Lab conducts research in audio, video, and haptic technologies, building systems that leverage their capabilities to facilitate and enrich both human-computer and computer-mediated human-human interaction. The lab is part of the Centre for Intelligent Machines and Department of Electrical and Computer Engineering of McGill University. McGill, one of Canada's most prestigious universities, is located in Montreal, a top city to live in, especially for students.
 
McGill University is committed to equity in employment and diversity. It welcomes applications from Aboriginal persons, persons with disabilities, ethnic minorities, persons of minority sexual orientation or gender identity, visible minorities, women and others who may contribute to diversification. All qualified applicants are encouraged to apply, although Canadians and permanent residents will be given priority.

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We have an open 4yr PhD position in the SPEAC labhttps://hrbosker.github.io (Speech Perception in Audiovisual Communication) at the Donders Institute, Radboud University, Nijmegen, The Netherlands.

The position is funded through an ERC Starting Grant (HearingHands, 101040276) awarded to Dr. Hans Rutger Bosker. We are looking for candidates with a strong background in speech perception and an interest in audiovisual prosody and gesture-speech integration.

You will work closely with Dr. Hans Rutger Bosker (PI) and Prof. James McQueen (promotor). The PhD project aims to determine how and when the timing of seemingly meaningless up-and-down hand gestures influences audiovisual speech perception, specifically targeting more naturalistic communicative settings. You will use virtual avatars, allowing careful control of their gestural movements, to establish which kinematic and communicative properties of hand gestures influence low-level speech perception. You will assess how challenging listening conditions impact the perceptual weighting of visual, auditory and audiovisual cues to prominence, as well as determine the time-course of these cues using eye-tracking. Finally, you will design training studies to test how humans adjust their perception to between-talker variation in gesture-speech alignment.

- 4 year contract, 1.0 FTE

- gross monthly salary: ? 2,541 - ? 3,247 (scale P)

- application deadline: May 22, 2023

- preferred starting date: September 1, 2023

More details about the project, profile, and what we have to offer is available through the link below. If you have any questions, do get in touch at HansRutger.Bosker@donders.ru.nl

https://www.ru.nl/en/working-at/job-opportunities/phd-candidate-audiovisual-speech-perception-at-the-donders-centre-for-cognition

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University of Washington, Seattle, WA, USA

Laboratory for Speech Physiology and Motor Control 

Post-doctoral position in speech sensorimotor learning in typical adults,

DBS patients, and adults who stutter

The Laboratory for Speech Physiology and Motor Control (PI Ludo Max, Ph.D.) at the University of Washington (Seattle) is seeking to fill a post-doctoral position in the areas of sensorimotor integration and sensorimotor learning for speech production. The position will involve experimental work on sensorimotor adaptation, sensory prediction, and error evaluation in typical adults, Parkinson’s and essential tremor patients with deep brain stimulation implants (DBS), and adults who stutter. The lab is located in the University of Washington's Department of Speech and Hearing Sciences and has additional affiliations with the Graduate Program in Neuroscience, the Department of Bioengineering, and the Department of Linguistics.

The successful candidate will use speech sensorimotor adaptation paradigms (with digital signal processing perturbations applied to the real-time auditory feedback or mechanical loads applied to the jaw by a robotic device) to investigate various aspects of motor learning and control. Data collection will involve acoustic, kinematic, and neural data to investigate auditory-motor interactions during speech movement planning and execution.

The appointment is initially for one year, with renewal possible contingent upon satisfactory performance and productivity. We are looking for a candidate available to start in the summer of 2023, and applicants should have completed all requirements for their Ph.D. degree by that time. Review of applications will begin immediately. Candidates with a Ph.D. degree in neuroscience, cognitive/behavioral neuroscience, motor control/kinesiology, biomedical engineering, communication disorders/speech science, and related fields, are encouraged to apply.

For more information, please contact lab director Ludo Max, Ph.D. (LudoMax@uw.edu). Applications can be submitted to the same e-mail address. Interested candidates should submit (a) a cover letter describing their research experiences, interests, and goals, (b) a curriculum vitae, (c) the names and contact information of three individuals who can serve as references, and (d) reprints of relevant journal publications.

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Dans le cadre d'un projet PIA, nous avons trois offres de post-doc d'une durée de 2 ans,
ouvertes sur le site toulousain : reconnaissance automatique de la parole, analyse de
sentiments et modélisation de préférences, pour un assistant vocal embarqué dans les
véhicules.

Plus d'info ici : https://www.irit.fr/~Thomas.Pellegrini/pdf/postdoc_3offers_2023.pdf

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Offre d’emploi : 1 Chargé.e de recherche et développement (H/F) Conversion neuronale de l’identité vocale pour la réalisation d’attaques adverses

Disponibilité et durée : 18 mois, de préférence à partir du 01 juin 2023

Description du poste: Dans le cadre du projet ANR BRUEL (2022-2026), l’équipe Analyse et Synthèse des sons recherche un.e chargé.e de recherche pour la conception, l’implémentation, et l’apprentissage d’algorithmes de conversion neuronale de l’identité vocale pour la création d’attaques d’usurpation d’identité. A partir d’un ensemble de scénarios d’attaques envisagées pour réaliser ces attaques en fonction des moyens et ressources disponibles (expertise, algorithmes, données), les travaux consisteront dans un premier temps à réaliser l’implémentation d’un banc d’essais d’algorithmes pour évaluer la robustesse des systèmes d’authentification et de détection face à ces attaques. Les travaux porteront dans un second temps sur l’une ou plusieurs des problématiques suivantes : - L’apprentissage de la conversion d’identité à partir de données de qualité hétérogène et dégradée (compression, bruits, etc…) librement accessibles (par exemple sur internet), et le transfert d’identité à partir de peu de données par des stratégies d’adaptation neuronale à partir de peu d’exemples; - La génération de conversions avec un contrôle de l’emprunte acoustique pour que l’attaque soit adaptée à l’environnement sonore et au canal de communication en fonction des scénarios envisagés (depuis des conditions professionnelles jusqu’à des conditions dégradées de communication téléphonique ou internet). L’ensemble des travaux réalisés seront évalués selon les protocoles usuels en conversion d’identité vocale, mais également en relation avec les partenaires du projet pour mesurer les performances des systèmes d’authentification/détection en fonction des scénarios envisagés. Les avancées réalisées seront intégrées au système de conversion neuronale de l'identité vocale de l’Ircam et évaluées in situ dans le cadre de productions professionnelles et/ou artistiques réalisées à l’Ircam. Le.a chargé.e de recherche collaborera également avec l’équipe de développement et participera aux activités du projet (évaluation des algorithmes, réunions, spécifications, livrables, rapports).

Présentation du projet BRUEL Le projet ANR BRUEL (ElaBoRation d’Une méthodologie d’EvaLuation des systèmes d’identification par la voix) concerne l’évaluation/certification des systèmes d’identification par la voix face aux attaques adverses. En effet, les systèmes de reconnaissance automatique du locuteur sont vulnérables non seulement à la parole produite artificiellement par synthèse vocale, mais aussi à d'autres formes d'attaques telles que la conversion d’identité vocale et la relecture. Les artefacts créés lors de la création ou la manipulation de ces attaques frauduleuses constituent les marques laissées dans le signal par les algorithmes de synthèse vocale permettant ainsi de distinguer la voix réelle originale d’une voix usurpée. Dans ces conditions, la détection de l'usurpation d'identité requiert d'évaluer les contre-mesures d'usurpation d'identité en même temps que les systèmes de reconnaissance du locuteur. Le projet BRUEL ambitionne de proposer la première méthodologie d’évaluation/certification des systèmes d'identification par la voix basée sur une approche Critères Communs.

Contexte de travail Le travail sera effectué à l’IRCAM au sein de l’équipe Analyse et Synthèse des sons encadré par Nicolas Obin et Axel ROEBEL (SU, CNRS, IRCAM). Le travail pourra être mené partiellement à distance, avec la nécessité d’une participation aux réunions d’avancement du projet. L'Ircam est une association à but non lucratif, associée au Centre National d'Art et de Culture Georges Pompidou, dont les missions comprennent des activités de recherche, de création et de pédagogie autour de la musique du XXème siècle et de ses relations avec les sciences et technologies. Au sein de l'unité mixte de recherche, UMR 9912 STMS (Sciences et Technologies de la Musique et du Son) commune à l’Ircam, à Sorbonne Université, au CNRS, et au Ministère de la Culture et de la Communication, des équipes spécialisées mènent des travaux de recherche et de développement informatique dans les domaines de l'acoustique, du traitement des signaux sonores, des sciences cognitives, des technologies d’interaction, de l’informatique musicale et de la musicologie.

L'Ircam est situé au centre de Paris à proximité du Centre Georges Pompidou au 1, Place Stravinsky 75004 Paris.

Expérience et compétences requises: Nous recherchons un.e candidat.e spécialisé.e en apprentissage de réseaux de neurones profonds et en traitement automatique de la parole ou en vision, de préférence en deep fakes. Le·a candidate devra avoir une thèse de doctorat en sciences informatiques dans les domaines de l’apprentissage par réseaux de neurones profonds, ainsi que des publications dans des conférences et revues reconnues dans le domaine. Le·a candidat·e idéal·e aura:

• Une solide expertise en apprentissage machine, et en particulier en réseaux de neurones profonds.

• Une bonne expérience en traitement automatique de la parole ; de préférence dans le domaine de la génération ou des deep-fakes;

• Maîtrise du traitement du signal audio-vidéo numérique;

• Une excellente maîtrise du langage de programmation Python, de l’environnement TensorFlow pour l’apprentissage de réseaux de neurones, et du calcul distribué sur des serveurs GPUs

• Excellente maîtrise de l’anglais scientifique parlé et écrit

• Autonomie, travail en équipe, productivité, rigueur et méthodologie

Salaire Selon formation et expérience professionnelle

Candidatures Prière d'envoyer une lettre de motivation et un CV détaillant le niveau d'expérience/expertise dans les domaines mentionnés ci-dessus (ainsi que tout autre information pertinente) à Nicolas.Obin@ircam.fr et Axel.Roebel@ircam.fr Date limite de candidature 31 mai 2023

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Offre d’emploi : 1 Chargé.e de recherche et développement (H/F) Génération de deep fakes audio-visuel

Disponibilité et durée : 15 mois, de préférence à partir du 01 juin 2023

Description du poste Dans le cadre du projet ASTRID DeTOX (2023-2025), l’équipe Analyse et Synthèse des sons recherche un.e chargé.e de recherche pourl’implémentation et l’apprentissage d’algorithmes pour la génération de deep fakes audio-visuels avec les missions suivantes : - Collection, implémentation, et apprentissage d’algorithmes représentatifs de l’état-del’art pour la génération de deep fakes audio et visuel - Implémentation d’un nouvel algorithme de génération de deep fakes audio-visuel avec synchronisation des deux modalités en particulier pour assurer la cohérence de la parole et du mouvement du lèvre et du bas du visage - Construction de bases de données audio-visuel des personnes ciblées et apprentissage de modèles de génération de deep fakes pour ces personnes Le.a chargé.e de recherche collaborera également avec l’équipe de développement et participera aux activités du projet (évaluation des algorithmes, réunions, spécifications, livrables, rapports). Présentation du projet DeTOX Les récents challenges ont montré qu'il était extrêmement difficile de mettre au point des détecteurs universels de vidéos hyper-truquées - à l'exemple des 'deep fakes' utilisés pour contrefaire l'identité d'une personne. Lorsque les détecteurs sont exposés à des vidéos générées par un algorithme nouveau, c'est-à-dire inconnu lors de la phase d'apprentissage, les performances sont encore extrêmement limitées. Pour la partie vidéo, les algorithmes examinent les images une par une, sans tenir compte de l'évolution de la dynamique faciale au cours du temps. Pour la partie vocale, la voix est générée de manière indépendante de la vidéo ; en particulier, la synchronisation audio-vidéo entre la voix et les mouvements des lèvres n'est pas prise en compte. Ceci constitue un point faible important des algorithmes de génération de vidéos hyper-truquées. Le présent projet vise à implémenter et à apprendre des algorithmes de détection de deepfakes personnalisés sur des individus pour lesquels on peut disposer et/ou fabriquer de nombreuses séquences audio-vidéo réelles et falsifiées. En se basant sur des briques technologiques de base en audio et vidéo récupérées de l'état de l'art, le projet se concentrera sur la prise en compte de l'évolution temporelle des signaux audio-visuels et de leur cohérence pour la génération et la détection. Nous souhaitons ainsi démontrer qu'en utilisant simultanément l’audio et la vidéo et en se focalisant sur une personne précise lors de l'apprentissage et de la détection, il est possible de concevoir des détecteurs efficaces même face à des générateurs encore non répertoriés. De tels outils permettront de scruter et de détecter sur le web d'éventuelles vidéos hyper-truquées de personnalités françaises importantes (président de la république, journalistes, chef d'étatmajor des armées, ...) et ce dès leur publication.

Contexte de travail Le travail sera effectué à l’IRCAM au sein de l’équipe Analyse et Synthèse des sons encadré par Nicolas Obin et Axel ROEBEL (SU, CNRS, IRCAM). Le travail pourra être mené partiellement à distance, avec la nécessité d’une participation aux réunions d’avancement du projet. L'Ircam est une association à but non lucratif, associée au Centre National d'Art et de Culture Georges Pompidou, dont les missions comprennent des activités de recherche, de création et de pédagogie autour de la musique du XXème siècle et de ses relations avec les sciences et technologies. Au sein de l'unité mixte de recherche, UMR 9912 STMS (Sciences et Technologies de la Musique et du Son) commune à l’Ircam, à Sorbonne Université, au CNRS, et au Ministère de la Culture et de la Communication, des équipes spécialisées mènent des travaux de recherche et de développement informatique dans les domaines de l'acoustique, du traitement des signaux sonores, des sciences cognitives, des technologies d’interaction, de l’informatique musicale et de la musicologie.

L'Ircam est situé au centre de Paris à proximité du Centre Georges Pompidou au 1, Place Stravinsky 75004 Paris.

Expérience et compétences requises Nous recherchons un.e candidat.e spécialisé.e en apprentissage de réseaux de neurones profonds et en traitement automatique de la parole ou en vision, de préférence en deep fakes. Le·a candidate devra avoir une thèse de doctorat en sciences informatiques dans les domaines de l’apprentissage par réseaux de neurones profonds, ainsi que des publications dans des conférences et revues reconnues dans le domaine. Le·a candidat·e idéal·e aura:

• Une solide expertise en apprentissage machine, et en particulier en réseaux de neurones profonds.

• Une bonne expérience en traitement automatique de la parole ou en vision ; de préférence dans le domaine des deep-fakes;

• Maîtrise du traitement du signal audio-vidéo numérique;

• Une excellente maîtrise du langage de programmation Python, de l’environnement TensorFlow pour l’apprentissage de réseaux de neurones, et du calcul distribué sur des serveurs GPUs

• Excellente maîtrise de l’anglais scientifique parlé et écrit

• Autonomie, travail en équipe, productivité, rigueur et méthodologie

Salaire Selon formation et expérience professionnelle

Candidatures Prière d'envoyer une lettre de motivation et un CV détaillant le niveau d'expérience/expertise dans les domaines mentionnés ci-dessus (ainsi que tout autre information pertinente) à Nicolas.Obin@ircam.fr et Axel.Roebel@ircam.fr Date limite de candidature 31 mai 2023

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PhD Title: SUMMA-Sound : SUMMarization of Activities of daily living using Sound-based activity recognition Partnership:

IMT Atlantique : Campus ☒ Brest ☐ Nantes ☐ Rennes Laboratory : Lab-STICC Doctoral school : ☒ SPIN ☐ 3MG Funding: IMT Atlantique, co-tutelle with Instituto Superior Técnico

Context : IMT Atlantique, internationally recognised for the quality of its research, is a leading general engineering school under the aegis of the French Ministry of Industry and Digital Technology, ranked in the three main international rankings (THE, SHANGHAI, QS). Located on three campuses, Brest, Nantes and Rennes, IMT Atlantique aims to combine digital technology and energy to transform society and industry through training, research and innovation. It aims to be the leading French higher education and research institution in this field on an international scale. With 290 researchers and permanent lecturers, 1000 publications and 18 M€ of contracts, it supervises 2300 students each year and its training courses are based on cutting-edge research carried out within 6 joint research units: GEPEA, IRISA, LATIM, LABSTICC, LS2N and SUBATECH. The proposed thesis is part of the research activities of the team RAMBO (Robot interaction, Ambient systems, Machine learning, Behaviour, Optimization) and of the laboratory Lab-STICC and the department of Computer Science of IMT Atlantique. Scientific context: The objective of this thesis is to develop a method for collecting and summarizing domestic health-related data relevant for medical diagnosis, in a non-intrusive manner using audio information. This research addresses the lack of existing practical tools for providing high-level succinct information to medical staff on the evolution of patients they follow for health diagnostic purposes. This research is based on the assumption that valuable diagnostic data can be collected by observing short- and long-term lifestyle changes and behavioural anomalies. It relies on the latest advances in the domains of audio-based activity recognition, summarization of human activity, and health diagnosis. Research on health diagnosis in domestic environments has already explored a variety of sensors and modalities for gathering data on human health indicators [5]. Nevertheless, audio-based activity recognition is notable for its less intrusive nature. Employing state-of-the-art sound-based activity recognition models [2] to monitor domestic human activity, the thesis will investigate and develop methods for summarization of human activity [3] in a human-understandable language, in order to produce easily interpretable data by doctors who, remotely, monitor their patients [4]. This work continues and fosters the research of the RAMBO team at IMT Atlantique on ambient systems, enabling well ageing at home for the elderly adults or dependent populations [1]. We expect this thesis to provide technology likely to relieve the burden on gerontologists and elderly-care facilities, and alleviate the caregiver shortage by offering some automatic support to the task of monitoring elderly or handicapped people, enabling them to age-at-home while still being followed by medical specialists using automated means. Expected contributions of the thesis Scientific goals: (1) Determine the set of human activities relevant for health diagnosis, (2) Implement a state-of-the-art model for audio-based activity recognition and validate its function by clinicians, (3) Develop a model for summarizing the evolution of human activity over time intervals of arbitrary duration (typically spanning from days to months and possibly years). Expected outcomes of the PhD: (1) A model for semantic summarization of human activity, based on sound recognition of activities of daily living. (2) A proof of concept for this model Candidate profile and required skills: • Master Degree in Computer Science (or equivalent) • Programming and Software Engineering skills (Python, Git, Software Architecture Design) • Data science skills • Machine learning skills • English speaking and writing skills References: [1] Damien Bouchabou. “Human activity recognition in smart homes : tackling data variability using context-dependent deep learning, transfer learning and data synthesis”. Theses. Ecole nationale supérieure Mines-Télécom Atlantique, May 2022. url: https://theses.hal.science/tel-03728064. [2] Detection and Classification of Acoustic Scenes and Events (DCASE). url: https://dcase.community/challenge2022/task-soundevent-detection-in-domestic-environments (visited on 07/01/2022). [3] P Durga et al. “When less is better: A summarization technique that enhances clinical effectiveness of data”. In: Proceedings of the 2018 International Conference on Digital Health. 2018, pp. 116–120. [4] Akshay Jain et al. “Linguistic summarization of in-home sensor data”. In: Journal of Biomedical Informatics 96 (2019), p. 103240. issn: 1532-0464. [5] Mostafa Haghi Kashani et al. “A systematic review of IoT in healthcare: Applications, techniques, and trends”. In: Journal of Network and Computer Applications 192 (2021), p. 103164. Work Plan: The thesis will be organised in the following steps: (1) Definition of pertinent sounds and activities for health diagnosis, (2) Hardware set-up, (3) Dataset constitution, (4) Activity recognition, (5) Diarization of activities, (6) Summarization, (7) Validation in a real environment. Application: To apply for this position, please send an email with your Curriculum Vitae, a document with your academic results (if possible), and a couple of lines describing your motivation to pursue a PhD to mihai[dot]andries[at]imt-atlantique[dot]fr before 16 May 2023. Additional Information :  Application deadline : 16 May 2023  Start date : Fall 2023  Contract duration: 36 months  Localisation - Location : Brest (France) and Lisbon (Portugal)  Contact(s) : Mihai ANDRIES (mihai[dot]andries[at]imt-atlantique.fr) Plinio Moreno (plinio[at]isr.tecnico.ulisboa.pt)

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We are looking for a PhD student who is interested in Artificial Intelligence, Machine Learning, Natural Language Processing and Human-Robot Interaction.  The doctoral student will work in a newly funded project at the  Department of Speech, Music and Hearing within the School of Electrical Engineering and Computer Science at KTH. The project is financed by the Swedish AI-program WASP (Wallenberg AI, Autonomous Systems and Software Program) which offers a graduate school with research visits, partner universities, and visiting lecturers.

The newly started project is titled 'Anticipatory Control in Conversational Human-Robot Interaction'. The aim of the project is to use self-supervised learning to develop generic language models for human-robot interaction and explore how such models can be used in real time to predict and anticipate human behavior and thereby improve the interaction. Whereas traditional language models in NLP (such as BERT, GPT) have focused on written language, we want to model multi-modal conversation, where aspects such as engagement, turn-taking, and incremental processing are of importance. This means that the models will have to process both text, audio and video, including aspects such as how the human users move and their facial expressions.

In collaboration with industry and other projects, we will then explore how such models can be used for social robotic applications. Another important focus will be on model analysis and visualization.

For more information about the position, see https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:623390/where:4/

If you have any questions, don’t hesitate to contact Prof. Gabriel Skantze

(skantze@kth.se)

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Nous proposons une offre de thèse en acoustique-aérodynamique-mécatronique de la parole, dans le cadre du projet ANR AVATARS (« Artificial Voice production: control of bio-inspired port-HAmilToniAn numeRical and mechatronic modelS », 2023-2027). Le sujet porte sur la  'Caractérisation du comportement vocal humain dans la parole et dans le chant sur banc mécatronique robotisé. Application au développement de plis vocaux biomimétiques.'

Pour plus d'information et pour candidater :
https://www.gipsa-lab.grenoble-inp.fr/~nathalie.henrich/docs/PhDposition_ANR-AVATARS_GIPSA_2023.pdf

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Nous recherchons pour l'année 2023-2024 une personne pour un CDD 50% enseignement et
recherche en Phonétique.

Les infos sont à retrouver ici:
https://emploi.univ-grenoble-alpes.fr/offres/enseignants-enseignants-chercheurs-contractuels/enseignant-e-chercheur-e-en-phonetique-1240660.kjsp?RH=TLK_CDD_ENS

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Nous recherchons un·e candidat·e pour une thèse sur la modélisation computationnelle du
lien perception-production en parole. La thèse sera codirigée par A. Basirat
(https://scalab.univ-lille.fr) et J. Diard (https://lpnc.univ-grenoble-alpes.fr/). Les
informations concernant le projet, les compétences attendues et la modalité de
candidature sont disponibles à l’adresse ci-dessous :

https://emploi.cnrs.fr/Offres/Doctorant/UMR9193-ANABAS-001/Default.aspx

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Job Offer: PhD Causal Machine Learning Applied to NLP and the Study of Large Language
Models.
Starting date: November 1st, 2023 (flexible)
Application deadline: From now until the position is filled
Interviews (tentative):  Beginning of June and latter if the position is still open
Salary: ~2000€ gross/month (social security included)
Mission: research oriented (teaching possible but not mandatory)
Place of work (no remote): Laboratoire d'Informatique de Grenoble, CNRS, Grenoble, France

Keywords: natural language processing, causal machine learning, interpretability,
analysis, robustness, large language models, controllability

Description:
Natural language processing (NLP) has undergone a paradigm shift in recent years, owing
to the remarkable breakthroughs achieved by large language models (LLMs). Despite being
purely 'correlation machines' [CorrelationMachine], these models have completely altered
the landscape of NLP by demonstrating impressive results in language modeling,
translation, and summarization. Nonetheless, the use of LLMs has also surfaced crucial
questions regarding their reliability and transparency. As a result, there is now an
urgent need to gain a deeper understanding of the mechanisms governing the behavior of
LLMs, to interpret their decisions and outcomes in principled and scientifically grounded
ways.

A promising direction to carry out such analysis comes from the fields of causal analysis
and causal inference [CausalAbstraction]. Examining the causal relationships between the
inputs, outputs, and hidden states of LLMs, can help to build scientific theories about
the behavior of these complex systems. Furthermore, causal inference methods can help
uncover underlying causal mechanisms behind the complex computations of LLMs, giving hope
to better interpret their decisions and understand their limitations [Rome].

Thus, the use of causal analysis in the study of LLMs is a promising research direction
to gain deeper insights into the workings of these models.
As a Ph.D student working on this project, you will be expected to develop a strong
understanding of the principles of causal inference and their application to machine
learning, see for example the invariant language model framework [InvariantLM]. You will
have the opportunity to work on cutting-edge research projects in NLP, contributing to
the development of more reliable and interpretable LLMs. It is important to note that the
Ph.D. research project should be aligned with your interests and expertise. Therefore,
the precise direction of the research can and will be influenced by the personal taste
and research goals of the students. It is encouraged that you bring your unique
perspective and ideas to the table.

SKILLS
Master degree in Natural Language Processing, computer science or data science.
Mastering Python programming and deep learning frameworks.
Experience in causal inference or working with LLMs
Very good communication skills in English, (French not needed).

SCIENTIFIC ENVIRONMENT
The thesis will be conducted within the Getalp teams of the LIG laboratory
(https://lig-getalp.imag.fr/). The GETALP team has a strong expertise and track record in
Natural Language Processing. The recruited person will be welcomed within the team which
offer a stimulating, multinational and pleasant working environment.
The means to carry out the PhD will be provided both in terms of missions in France and
abroad and in terms of equipment. The candidate will have access to the cluster of GPUs
of both the LIG. Furthermore, access to the National supercomputer Jean-Zay will enable
to run large scale experiments.
The Ph.D. position will be co-supervised by Maxime Peyrard and François Portet.
Additionally, the Ph.D. student will also be working with external academic collaborators
at EPFL and Idiap (e.g., Robert West and Damien Teney)

INSTRUCTIONS FOR APPLYING
Applications must contain: CV + letter/message of motivation + master notes + be ready to
provide letter(s) of recommendation; and be addressed to Maxime Peyrard
(maxime.peyrard@epfl.ch) and François Portet (francois.Portet@imag.fr)


[InvariantLM] Peyrard, Maxime and Ghotra, Sarvjeet and Josifoski, Martin and Agarwal,
Vidhan and Patra, Barun and Carignan, Dean and Kiciman, Emre and Tiwary, Saurabh and
West, Robert, 'Invariant Language Modeling' Conference on Empirical Methods in Natural
Language Processing (2022): 5728–5743

[CorrelationMachine] Feder, Amir and Keith, Katherine A. and Manzoor, Emaad and Pryzant,
Reid and Sridhar, Dhanya and Wood-Doughty, Zach and Eisenstein, Jacob and Grimmer, Justin
and Reichart, Roi and Roberts, Margaret E. and Stewart, Brandon M. and Veitch, Victor and
Yang, Diyi, 'Causal Inference in Natural Language Processing: Estimation, Prediction,
Interpretation and Beyond' Transactions of the Association for Computational Linguistics
(2022), 10:1138–1158.

[CausalAbstraction] Geiger, Atticus and Wu, Zhengxuan and Lu, Hanson and Rozner, Josh and
Kreiss, Elisa and Icard, Thomas and Goodman, Noah and Potts, Christopher, 'Inducing
Causal Structure for Interpretable Neural Networks' Proceedings of Machine Learning
Research (2022): 7324-7338.

[Rome] Meng, Kevin, et al. 'Locating and Editing Factual Associations in GPT.' Advances
in Neural Information Processing Systems 35 (2022): 17359-17372.

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The application deadline is ** June 2, 2023 **

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In the framework of the PEPR Santé numérique “Autonom-Health” project (Health, behaviors and autonomous digital technologies), the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) and the LORIA (Nancy, France) are looking for candidates for a fully funded PhD position (36 months).  

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Open faculty position at KU Leuven, Belgium: junior professor in Synergistic Processing
of Multisensory Data for Audio-Visual Understanding

KU Leuven's Faculty of Engineering Science has an open position for a junior professor
(tenure track) in the area of audiovisual understanding. The successful candidate will
conduct research on synergetic processing of multisensory data for audio-visual
understanding, teach courses in the Master of Engineering Science and supervise students
in the Master and PhD programs. The candidate will be embedded in the PSI research
division of the Department of Electrical Engineering. More information is available at
https://www.kuleuven.be/personeel/jobsite/jobs/60193566?hl=en&lang=en . The deadline for
applications is September 29, 2023.

KU Leuven is committed to creating a diverse environment. It explicitly encourages
candidates from groups that are currently underrepresented at the university to submit
their applications.

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PhD in ML/NLP – Fairness and self-supervised learning for speech processing
Starting date: October 1st, 2023 (flexible)
Application deadline: June 9th, 2023
Interviews (tentative): June 14th, 2023

Salary: ~2000€ gross/month (social security included)

Mission: research oriented (teaching possible but not mandatory)

Keywords: speech processing, fairness, bias, self-supervised learning, evaluation metrics 

CONTEXT

This thesis is in the context of the ANR project E-SSL (Efficient Self-Supervised Learning for Inclusive and Innovative Speech Technologies). Self-supervised learning (SSL) has recently emerged as one of the most promising artificial intelligence (AI) methods as it becomes now feasible to take advantage of the colossal amounts of existing unlabeled data to significantly improve the performances of various speech processing tasks.

PROJECT OBJECTIVES

Speech technologies are widely used in our daily life and are expanding the scope of our action, with decision-making systems, including in critical areas such as health or legal aspects. In these societal applications, the question of the use of these tools raises the issue of the possible discrimination of people according to criteria for which society requires equal treatment, such as gender, origin, religion or disability...  Recently, the machine learning community has been confronted with the need to work on the possible biases of algorithms, and many works have shown that the search for the best performance is not the only goal to pursue [1]. For instance, recent evaluations of ASR systems have shown that performances can vary according to the gender but these variations depend both on  data used for learning and on models [2]. Therefore such systems are increasingly scrutinized for being biased while trustworthy speech technologies definitely represents a crucial expectation.

 Both the question of bias and the concept of fairness have now become important aspects of AI, and we now have to find the right threshold between accuracy and the measure of fairness. Unfortunately, these notions of fairness and bias are challenging to define and their
 meanings can greatly differ [3].

 The goals of this PhD position are threefold:

- First make a survey on the many definitions of robustness, fairness and bias with the aim of coming up with definitions and metrics fit for speech SSL models

- Then gather speech datasets with high amount of well-described metadata

- Setup an evaluation protocol for SSL models and analyzing the results.

SKILLS

• Master 2 in Natural Language Processing, Speech Processing, computer science or data science.

• Good mastering of Python programming and deep learning framework.

• Previous experience in bias in machine learning would be a plus

• Very good communication skills in English

• Good command of French would be a plus but is not mandatory 

SCIENTIFIC ENVIRONMENT

The PhD position will be co-supervised by Alexandre Allauzen (Dauphine Université PSL, Paris) and Solange Rossato and François Portet (Université Grenoble Alpes). Joint meetings are planned on a regular basis and the student is expected to spend time in both places. Moreover, two other PhD positions are open in this project.  The students, along with the partners will closely collaborate. For instance, specific SSL models along with evaluation criteria will be developed by the other PhD students. Moreover, the PhD student will collaborate with several team members involved in the project in particular the two other PhD candidates who will be recruited  and the partners from LIA, LIG and Dauphine Université PSL, Paris. The means to carry out the PhD will be provided both in terms of missions in France and abroad and in terms of equipment. The candidate will have access to the cluster of GPUs of both the LIG and Dauphine Université PSL. Furthermore, access to the National supercomputer Jean-Zay will enable to run large scale experiments.

INSTRUCTIONS FOR APPLYING

Applications must contain: CV + letter/message of motivation + master notes + be ready to provide letter(s) of recommendation; and be addressed to Alexandre Allauzen (alexandre.allauzen@espci.psl.eu), Solange Rossato (Solange.Rossato@imag.fr) and François Portet (francois.Portet@imag.fr). We celebrate diversity and are committed to creating an inclusive environment for all employees.

REFERENCES:

[1] Mengesha, Z., Heldreth, C., Lahav, M., Sublewski, J. & Tuennerman, E. “I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans. Frontiers in Artificial Intelligence 4. issn: 2624-8212. https://www.frontiersin.org/article/10.3389/frai.2021.725911 (2021).

[2] Garnerin, M., Rossato, S. & Besacier, L. Investigating the Impact  of Gender Representation in ASR Training Data: a Case Study on Librispeech in Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing (2021), 86–92.
[3] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A Survey on Bias and Fairness in Machine Learning.  ACMComput. Surv. 54. issn: 0360-0300. https://doi.org/10.1145/3457607 (July 2021).

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In the framework of the European FETPROACT « Fvllmonti » project and the PEPR Santé numérique “Autonom-Health” project, the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) is looking for candidates for a 24-months post-doctoral position. 

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ELDA (Evaluations and Language resources Disctribution Agency) looks for full/part time transcriptors for transcription of phone calls in the financial domain.

Location: ELDA (Paris-France)

Latest starting date: July 2023

Languages and mission details

• German - 21h of speech to be transcribed + phonetisation task;
• Spanish - 1h of speech to be transcribed;
• Italian - 1h of speech to be transcribed;
• Japanese - 21h of speech to transcribe + phonetisation task;
• Polish - number of hours vary as it is still being tested.

Profile

• Native expertise in the selected language with a very good level of spelling and grammar;
• Good knowledge of French and/or English;
• Good computer skills;
• Ability to integrate and scrupulously follow transcription rules.

Salary and duration

• Starting from SMIC depending on skills;
• Approximately 3 months (FT for the longer assignments).

Application

• Send your CV to <lucille@elda.org> ou <gabriele@elda.org>;
• Include [Transcription *language*] in the object field.

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DRhyaDS

A new framework for understanding the Dynamic Rhythms and Decoding of Speech

Job Title - Postdoctoral Researcher

Disciplines and Areas of Research - Speech science, Psycholinguistics, Psychoacoustics

Contract Duration - 1 Year

Research Overview: The DRhyaDS project aims to develop a new framework for understanding the dynamic rhythms and decoding of speech. It focuses on exploring the temporal properties of speech and their contribution to speech perception. The project challenges the conventional view that speech rhythm perception relies on a one-to-one association between specific modulation frequencies in the speech signal and linguistic units. One of the key objectives of the project is to investigate the impact of language-specific temporal characteristics on speech dynamics. The project team will analyze two corpora of semi-spontaneous speech data from French and German, representing syllable-timed and stress-timed languages, respectively. Various acoustic analyses will be conducted on these speech corpora to explore the variability of slow temporal modulations in speech at an individual level. This comprehensive acoustic exploration will involve extracting and analyzing prosody, spectral properties, temporal dynamics, and rhythmic patterns. By examining these acoustic parameters, the project aims to uncover intricate details about the structure and variation of speech signals across languages and speakers, contributing to a more nuanced understanding of the dynamic nature of spoken language and its role in human communication. Environment: The selected candidate will be an integral part of an international research team and will work in a collaborative and stimulating lab environment. The project brings together a FrancoGerman team of experts in linguistics, psychoacoustics and cognitive neuroscience, led by Dr. Léo Varnet (CNRS, ENS Paris) and Dr. Alessandro Tavano (Max Planck Institute, Goethe University Frankfurt). The successful candidate will work under the supervision of Dr Léo Varnet, at the Laboratoire des Systèmes Perceptifs (ENS Paris).

Job description: This is a one-year postdoctoral contract position, offering a net salary in accordance with French legislation (~2500€/month + social and medical benefits). Women and minorities are strongly encouraged to apply. The successful candidate will participate in research activities, collaborate with team members, and contribute to scientific publications and communications. Additionally, they will have the autonomy to suggest and implement their own analysis techniques and approaches. Their responsibilities will include: - Taking a lead role in collecting a comprehensive corpus of French speech data, adhering to a rigorous data collection protocol - Collaborating closely with the German team to leverage the existing German speech corpus for comparative analysis and cross-linguistic investigations - Conducting in-depth acoustic analysis of the corpora, employing advanced techniques to investigate the variability and dynamics of slow temporal modulations in speech - Actively participating in team meetings, workshops, and conferences to present research progress, exchange ideas, and contribute to the intellectual growth of the project - Engaging in science outreach activities to promote the project's research outcomes and facilitate public understanding of speech perception and language processing.

Qualifications: - A recently obtained PhD in a relevant field (e.g., linguistics, psychology, neuroscience, computational sciences) - Strong expertise in linguistics, speech perception, acoustic analysis, and statistical methods - Proficiency in programming languages commonly used in speech research. Knowledge of MATLAB would be particularly valuable for data processing and analysis within the project. - Strong written and verbal communication skills in English. Candidates with proficiency in French and/or German language skills would be particularly appreciated, as it would enable a deeper understanding of the linguistic characteristics of the respective corpora.

Application process To apply for this position, please submit a CV and a cover letter (in French or English) along with the names and contact information of 2 referees to Léo Varnet (leo.varnet@cnrs.fr). The application deadline is 31th July 2023. Interviews will be conducted in September. The ideal start date is October-November 2023, with some flexibility allowed. Feel free to get in touch informally to discuss this position

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Inria is opening a fully funded PhD position on multimodal speech
anonymization. For details and to apply, see:
https://jobs.inria.fr/public/classic/en/offres/2023-06410

Applications will be reviewed on a continuous basis until July 2.

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