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ISCApad #285 |
Tuesday, March 08, 2022 by Chris Wellekens |
6-1 | (2021-10-20) PhD position Orléans/Grenoble France Lieu : Orléans/Grenoble, France Selon les avancées, les recherches pourront s'étendre à d'autres tâches du TAL : Bibiographie sélective Encadrement Calendrier : Date limite d?envoi des dossiers : 05 novembre 2021 Les dates des auditions seront communiquées aux candidat.e.s retenu.e.s sur dossier.
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6-2 | (2021-10-21) Three fully-funded PhD positions on early prosodic development at Utrecht University The Netherlands Three fully-funded PhD positions on early prosodic development at Utrecht University
These positions are part of a new Dutch Research Council project ‘SoundStart’ on early prosodic development led by Professor Aoju Chen at Utrecht University. Children show knowledge of prosody (i.e., melody and rhythm) of their native language already at birth and thrive on it in early language and communication development. However, how children learn prosody so early is still unknown. Taking an interdisciplinary and crosslinguistic approach, SoundStart aims to discern the role of innate mechanisms, uncover learning mechanisms in the auditory modality underlying development spanning prenatal and after-birth periods, and shed light on the role of visual cues to prosody (i.e., speech-accompanying gestures) in after-birth periods. PhD projects 1 and 2 will be concerned with the first two goals in the learning of prosodic phrasing (i.e., grouping sequences of sounds into meaningful units in speech streams) and prosodic form-meaning mappings (i.e. associating prosodic patterns with communicative functions), PhD project 3 with the third goal in both areas of prosodic development. We are seeking to hire highly motivated, driven and talented MA graduates to take on the PhD projects, starting 1 February 2022. A later starting date is negotiable.
In this research programme you will work within an interdisciplinary team, closely collaborating with researchers from linguistics, neuroscience, psycholinguistics, psychology and neonatology at Utrecht University and the University Medical Centre Utrecht. You will receive assistance from student assistants in data collection (in the Netherlands and abroad) and/or data processing and have the opportunity to develop your academic teaching skills during your project.
Do you want to play a key role in this new exciting research programme on early prosodic development? Read more and apply! The application deadline is 8 November, 2021.
For more information about these positions, please contact Professor Aoju Chen at aoju.chen@uu.nl.
---- Prof. dr. Aoju Chen Chair of Language Development in Relation to Socialisation and Identity Head of the English Language and Culture Division Leader of VICI research group SoundStart Department of Languages, Literature and Communication & Utrecht Institute of Linguistics - OTS Utrecht University The Netherlands
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6-3 | (2021-10-25) Ingenieur de développement, INRIA,Nancy, France L'équipe Multispeech d'Inria Nancy recrute un ingénieur permanent dont la mission principale sera de piloter et contribuer au développement d'un assistant virtuel multimodal évolutif, à partir de briques logicielles open source et/ou issues de l'équipe, à des fins de recherche et de transfert industriel.
Fiche de poste: voir pages 9-10 du fichier https://www.inria.fr/sites/default/files/2021-10/Fiches_poste_ingenieurs_permanents.pdf
Candidature en ligne: https://www.inria.fr/fr/concours-externes Date butoir: 31/10/2021.
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6-4 | (2021-10-26) Master internships at TALEP, Marseille, France L'équipe TALEP de Marseille propose plusieurs stages de M2 en TAL à partir de février 2022 :
The TALEP team (Marseille, France) has several master internship offers in NLP starting in Feb 2022:
* Joint speech segmentation and syntactic analysis
* Syntactic analysis of speech without transcription* Matching contextual and definitional embeddings for a sense-aware reading assistant * Using deep learning to study children?s multimodal behavior in face-to-face conversation * Using interpretability methods to explain Vision-Language models for medical applications * Impact of language evolution in historical texts on NLP models * Fidélité et exactitude de la génération dans les systèmes de génération de texte
* ...
Liste complète et détails / full list and details : https://www.lis-lab.fr/offre-de-stage/
Candidatez maintenant pour découvrir un environnement scientifique stimulant dans une ville vibrante et ensoleillée :-)
Apply now to discover a stimulating scientific environment in a vibrant sunny city :-)
-- Carlos RAMISCH
http://pageperso.lis-lab.fr/carlos.ramisch
Assistant professor at LIS/TALEP and Aix Marseille University, France
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6-5 | (2021-11-12) Internships at IRIT, Toulouse, France L’équipe SAMoVA de l’IRIT à Toulouse propose plusieurs stages en 2022 :
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6-6 | (2021-11-13)Master internship at IRISA, Lannion, France EXPRESSION Team IRISA LANNION - Proposal for an internship for a Research Master in Computer Science
Title: Joint training framework for text-to-speech and voice conversion
Text-to-speech and voice conversion are two distinct speech generation techniques. Text-to-speech (TTS) is a process that generates speech from a sequence of graphemes or phonemes. Voice conversion is the conversion of speech from a source voice to a target voice. These processes find their application in domains such as Computer Assisted Language Learning, for example.
However, these two processes share some bricks, particularly the vocoder that generates speech from acoustic characteristics or the spectrogram. The quality of these two technologies has been significantly improved thanks to the availability of massive databases, the power of computing machines, and the deep learning paradigm implementation. On the other hand, restoring or controlling expressiveness, and more generally considering suprasegmental information, remains a major challenge for these two technologies.
This internship topic aims at setting up a common framework for both technologies. We aim at a joint deep learning framework to generate speech (target voice) from either speech (source voice) or text.
It will be supervised by members of the EXPRESSION team (IRISA): Aghilas Sini, Pierre Alain, Damien Lolive, and Arnaud Delhay-Lorrain.
Please send your application (CV + cover letter) before 10/01/2022 to à aghilas.sini@irisa.fr,palain@irisa.fr, damien.lolive@irisa.fr, arnaud.delhay@irisa.fr
Start date: 01/02/2022 (flexible)
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6-7 | (2021-11-15) Stage en reconnaissance automatique de la parole chez Zaion, France ====== Offre de stage en reconnaissance automatique de la parole ======== Nous proposons une offre de stage au sein de Zaion, portant sur le développement de solutions de Reconnaissance Automatique de la Parole adaptées au contexte de la relation client, sur de nouvelles langues (niveau M2).
Merci de transférer si vous connaissez des étudiant.e.s à la quête de telle opportunité. Description et contacts :
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6-8 | (2021-11-26) Four research internships at SteelSeries France R&D team The SteelSeries France R&D team (former Nahimic R&D team) is glad to open 4 research internship positions for 2022. The selected candidates will be working on one of the following topics (more details in attached):
- Audio media classification
- Audio source classification
- Audio source separation
- Real-time speech restoration
Please reply/apply to nathan.souviraa-labastie@steelseries.com.
Audio media classification Master internship, Lille (France), 2022
Advisors — Pierre Biret, R&D Engineer, pierre.biret@steelseries.com — Nathan Souviraà-Labastie, R&D Engineer, PhD, nathan.souviraa-labastie@steelseries.com
Company description
SteelSeries is a leader in gaming peripherals focused on quality, innovation and functionality, and the fastest growing major gaming headset brand globally. Founded in 2001, SteelSeries improves performance through first-to-market innovations and technologies that enable gamers to play harder, train longer, and rise to the challenge. SteelSeries is a pioneer supporter of competitive gaming tournaments and eSports and connects gamers to each other, fostering a sense of community and purpose. Nahimic has joined the SteelSeries family in 2020 to bolster reputation of industry-leading gaming audio performance across both hardware and software. Nahimic is the leading 3D gaming audio software editor with more than 150 man-years of research and development in gaming industry. Their team gathers the rare combination of world class audio processing engineers and software geniuses based across France, Singapore and Taiwan. They are the worldwide leader in PC audio gaming software that are embedded in millions of gaming devices, from gaming headsets to the most powerful gaming PCs by brands such as MSI, Dell, Asus, Gigabyte, etc. Their technology offers the most precise and immersive sound for gamers that allows them to be more efficient in any game and have more immersive feeling. We wish to meet passionate people full of energy and motivations, ready to achieve great challenges to exhale everyone’s audio experience. We are currently looking for a AUDIO SIGNAL PROCESSING / MACHINE LEARNING RESEARCH INTERN to join the R&D team of SteelSeries’ Software & Services Business Unit in our French office (former Nahimic R&D team).
Subject
The target of the internship is to build a model able to classify audio streams into multiple media classes (classes description upon request).The audio classification problem will be addressed using supervised machine learning. Hence, the first step of the internship will be to collect data and build a balanced corpus for such an audio classification problem. Fortunately, massive audio content for most potential classes are available within the company and this task should not be an important burden. Once the corpus is built, the intern will have to either tune the parameters of an already existing internal model or develop a more adapted model from the state of the art [1] [2] [4] that still satisfy the «real-time» constraint. A more advanced step of the internship would be to define a more precise media type classification with for instance sub-types within a same category.Once the relevant classes have been identified, the intern will have to incorporate such changes in his classification algorithm and framework. As the intern will receive support to turn his model into an in-product real-time prototype, this internship is a rare opportunity to bring research to product in such a short time frame.
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 algorithms for digital audio signal processing. Machine learning skills is a plus. 1 Whereas not mandatory, notions in the following various fields would be appreciated : Audio, acoustics and psychoacoustics - Audio effects in general : compression, equalization, etc. - Machine learning and artificial neural networks. - Statistics, probabilist approaches, optimization. - Programming language : Matlab, Python, Pytorch, Keras, Tensorflow. - 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.
Other offers https://nahimic.welcomekit.co/
https://www.welcometothejungle.co/companies/nahimic/jobs
Références
[1] DCase Challenge Low-Complexity Acoustic Scene Classification with Multiple Devices. url : http: //dcase.community/challenge2021/task-acoustic-scene-classification-results-a.
[2] B. Kim et al. Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization. 2021. arXiv : 2111.06531 [cs.SD].
[3] Nahimic on MSI. url : https://fr.msi.com/page/nahimic.
[4] sharathadavanne. seld-dcase2021. https://github.com/sharathadavanne/seld-dcase2021. 2021
Audio source classification Master internship, Lille (France), 2022
Advisors — Nathan Souviraà-Labastie, R&D Engineer, PhD, nathan.souviraa-labastie@steelseries.com — Pierre Biret, R&D Engineer, pierre.biret@steelseries.com Company description
SteelSeries is a leader in gaming peripherals focused on quality, innovation and functionality, and the fastest growing major gaming headset brand globally. Founded in 2001, SteelSeries improves performance through first-to-market innovations and technologies that enable gamers to play harder, train longer, and rise to the challenge. SteelSeries is a pioneer supporter of competitive gaming tournaments and eSports and connects gamers to each other, fostering a sense of community and purpose. Nahimic has joined the SteelSeries family in 2020 to bolster reputation of industry-leading gaming audio performance across both hardware and software. Nahimic is the leading 3D gaming audio software editor with more than 150 man-years of research and development in gaming industry. Their team gathers the rare combination of world class audio processing engineers and software geniuses based across France, Singapore and Taiwan. They are the worldwide leader in PC audio gaming software that are embedded in millions of gaming devices, from gaming headsets to the most powerful gaming PCs by brands such as MSI, Dell, Asus, Gigabyte, etc. Their technology offers the most precise and immersive sound for gamers that allows them to be more efficient in any game and have more immersive feeling. We wish to meet passionate people full of energy and motivations, ready to achieve great challenges to exhale everyone’s audio experience. We are currently looking for a AUDIO SIGNAL PROCESSING / MACHINE LEARNING RESEARCH INTERN to join the R&D team of SteelSeries’ Software & Services Business Unit in our French office (former Nahimic R&D team).
Subject
The target of the internship is to build a model able to classify audio sources. And by audio sources, we mean sources present inside a predetermined given media such as music, movies or video games, e.g., instruments in the case of music. The audio classification problem will be addressed using supervised machine learning. The intern would not start his project from scratch as data and classification code from other projects can be re-used with minor adaptation (description upon request). Once the corpus is reshaped for classification, the intern will have to either tune the parameters of an already existing internal model or develop a more adapted model from the state of the art [1] [3] [5] that still satisfy strong real-time constraint. Multi-task approach A more advanced step of the internship would be to explore multi-task models. The two tasks of target would be 1/ the classification task that the intern would have previously addressed, 2/ the audio source separation task on the same data type. This is a very challenging machine learning problem, especially because the different tasks are heterogeneous (classification, regression, signal estimation), contrary to homogeneous multi-task classification where a classifier is able to address different classification task. Moreover, just a few study are targeting audio heteregeneous multi-task (exhaustive list from advisors knowledge [2, 6, 7, 4]). Potential advantages of the multi-task approach are performance improvement for the main/principal task and computational cost reduction in products, as several tasks are achieved at the same time. Previous internal bibliographic work and network architecture could be used as starting point for this approach.
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 algorithms for digital audio signal processing. Machine learning skills is a plus. Whereas not mandatory, notions in the following various fields would be appreciated : Audio, acoustics and psychoacoustics - Machine learning and artificial neural networks. - Audio effects in general : compression, equalization, etc. - Statistics, probabilist approaches, optimization. - Programming language : Matlab, Python, Pytorch, Keras, Tensorflow. - Sound spatialization effects : binaural synthesis, ambisonics, artificial reverberation. - Voice recognition, voice command. - Voice processing effects : noise reduction, echo cancellation, array processing. - 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.
Other offers
https://nahimic.welcomekit.co/ https://www.welcometothejungle.co/companies/nahimic/jobs
Références
[1] DCase Challenge Low-Complexity Acoustic Scene Classification with Multiple Devices. url : http: //dcase.community/challenge2021/task-acoustic-scene-classification-results-a.
[2] P. Georgiev et al. « Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations ». In : Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1.3 (sept. 2017), 50 :1-50 :19.
[3] B. Kim et al. Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization. 2021. arXiv : 2111.06531 [cs.SD].
[4] H. Phan et al. « On multitask loss function for audio event detection and localization ». In : arXiv preprint arXiv :2009.05527 (2020).
[5] sharathadavanne. seld-dcase2021. https://github.com/sharathadavanne/seld-dcase2021. 2021.
[6] G. P. Stéphane Dupont Thierry Dutoit. « Multi-task learning for speech recognition : an overview ». In : 24th European Symposium on Artificial Neural Networks 1 (2016).
[7] D. Stoller, S. Ewert et S. Dixon. « Jointly Detecting and Separating Singing Voice : A Multi-Task Approach ». en. In : arXiv :1804.01650 [cs, eess] (avr. 2018). arXiv : 1804.01650.
Audio source 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
SteelSeries is a leader in gaming peripherals focused on quality, innovation and functionality, and the fastest growing major gaming headset brand globally. Founded in 2001, SteelSeries improves performance through first-to-market innovations and technologies that enable gamers to play harder, train longer, and rise to the challenge. SteelSeries is a pioneer supporter of competitive gaming tournaments and eSports and connects gamers to each other, fostering a sense of community and purpose. Nahimic has joined the SteelSeries family in 2020 to bolster reputation of industry-leading gaming audio performance across both hardware and software. Nahimic is the leading 3D gaming audio software editor with more than 150 man-years of research and development in gaming industry. Their team gathers the rare combination of world class audio processing engineers and software geniuses based across France, Singapore and Taiwan. They are the worldwide leader in PC audio gaming software that are embedded in millions of gaming devices, from gaming headsets to the most powerful gaming PCs by brands such as MSI, Dell, Asus, Gigabyte, etc. Their technology offers the most precise and immersive sound for gamers that allows them to be more efficient in any game and have more immersive feeling. We wish to meet passionate people full of energy and motivations, ready to achieve great challenges to exhale everyone’s audio experience. We are currently looking for a AUDIO SIGNAL PROCESSING / MACHINE LEARNING RESEARCH INTERN to join the R&D team of SteelSeries’ Software & Services Business Unit in our French office (former Nahimic R&D team).
Approaches and topics for the internship 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 [11, 1] were equaling the state of the art [12, 13] on a music separation task and many tracks of improvement are possible in terms of implementations (details hereafter). The selected candidate will work on one or several of the following topics according to her/his aspirations, skills and bibliographic outcomes. In addition to those topics, the candidates can also make their own topic proposal. She/he will also have the chance to work on our internal substantive datasets. New core algorithm Machine learning is a fast changing research domain and an algorithm can move from being state of the art to being obsolete in less than a year (see for instance the recent advances in music source separation [9, 3]). The task would be to try recent powerful neural network approaches like recent architectures or unit types that proved benefit in other research fields. For instance, the encoding and decoding part of [15] shows huge benefit compared to traditional audio codec. Other research domains outside audio (like computer vision) might be considered as sources of inspiration. For instance, the approaches in [14, 6] have shown promising results on other tasks and previous internal work [1] managed to bring those benefits to audio source separation. Conversely, approaches like [10, 5] were tested without benefit for the separation tasks that we target. 1 Overall, the targeted benefits of a new approach can be of two kinds, either to bring improvements in terms of audio separation performances, either to reduce the computational costs (mainly CPU/GPU load, RAM usage). Extension to multi-source Another challenging problem would be to estimate all the different sources with a single network, either by selecting wich source to output but with a single network such as in [7], either by outputting all sources at the same time. In the case of music, most of the state of the art approaches [12] had historically addressed the backing track problem (i.e., karaoke for instruments) as a one instrument versus the rest problem, hence using specific networks for each instruments when multiple instruments are present in the mix. Pruning Beside testing new architectures or unit types, pruning could be a simple and effective way to reduce computational costs. The original pruning principle is to remove the less influent neural units in order to avoid overfitting. We would mainly be interested in reducing the total amount of units and parameters.The theoretical and domain agnostic literature [16, 4, 8], as well as the audio specific literature [2] will be explored. As the selected candidate would work on our most advanced model, this subject is the opportunity to have a direct impact on the company in such a short time frame.
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 algorithms for digital audio signal processing. Machine learning skills is a plus. Whereas not mandatory, notions in the following various fields would be appreciated : Audio, acoustics and psychoacoustics - Machine learning and artificial neural networks. - Audio effects in general : compression, equalization, etc. - Statistics, probabilist approaches, optimization. - Programming language : Matlab, Python, Pytorch, Keras, Tensorflow. - Sound spatialization effects : binaural synthesis, ambisonics, artificial reverberation. - Voice recognition, voice command. - Voice processing effects : noise reduction, echo cancellation, array processing. - 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.
Other offers
https://nahimic.welcomekit.co/ https://www.welcometothejungle.co/companies/nahimic/jobs
Internship position at Telecom-Paris on Deep learning approaches for social computing
*Place of work* Telecom Paris, Palaiseau (Paris outskirt)
*Starting date* From February 2021(but can start later)
*Duration* 4-6 months
*Context* The intern will take part in the REVITALISE project, funded by ANR. The research activity of the internship will bring together the research topics of Prof. Chloé Clavel [Clavel] of the S2a [SSA] team at Telecom-Paris– social computing [SocComp] - and Dr. Mathieu Chollet [Chollet] from University of Glasgow – multimodal systems for social skills training, and Dr Beatrice Biancardi [Biancardi] – Social Behaviour Modelling from CESI Engineering School, Nanterre.
* Candidate profile* As a minimum requirement, the successful candidate should have: • A master degree in one or more of the following areas: human-agent interaction, deep learning, computational linguistics, affective computing, reinforcement learning, natural language processing, speech processing • Excellent programming skills (preferably in Python) • Excellent command of English • The desire to do an academic thesis at Telecom-Paris after the internship
*How to apply* The application should be formatted as **a single pdf file** and should include: • A complete and detailed curriculum vitae • A cover letter • The contact of two referees The pdf file should be sent to the two supervisors: Chloé Clavel, Beatrice Biancardi and Mathieu Chollet: chloe.clavel@telecom-paris.fr, bbiancardi@cesi.fr, mathieu.chollet@glasgow.ac.uk
Multimodal attention models for assessing and providing feedback on users’ public speaking ability
*Keywords* human-machine interaction, attention models, recurrent neural networks, Social Computing, natural language processing, speech processing, non-verbal behavior processing, multimodality, soft skills, public speaking
*Supervision* Chloé Clavel, Mathieu Chollet, Beatrice Biancardi
*Description* Oral communication skills are essential in many situations and have been identified as core skills of the 21st century. Technological innovations have enabled social skills training applications which hold great training potential: speakers’ behaviors can be automatically measured, and machine learning models can be trained to predict public speaking performance from these measurements and subsequently generate personalized feedback to the trainees. The REVITALISE project proposes to study explainable machine learning models for the automatic assessment of public speaking and for automatic feedback production to public speaking trainees. In particular, the recruited intern will address the following points: - identify relevant datasets for training public speaking and prepare them for model training - propose and implement multimodal machine learning models for public speaking assessment and compare them to existing approaches in terms of predictive performance. - integrate the public assessment models to produce feedback a public speaking training interface, and evaluate the usefulness and acceptability of the produced feedback in a user study The results of the project will help to advance the state of the art in social signal processing, and will further our understanding of the performance/explainability trade-off of these models.
The compared models will include traditional machine learning models proposed in previous work [Wortwein] and sequential neural approaches (recurrent networks) that integrate attention models as a continuation of the work done in [Hemamou], [BenYoussef]. The feedback production interface will extend a system developed in previous work [Chollet21].
Selected references of the team: [Hemamou] L. Hemamou, G. Felhi, V. Vandenbussche, J.-C. Martin, C. Clavel, HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews. in AAAI 2019, to appear [Ben-Youssef] Atef Ben-Youssef, Chloé Clavel, Slim Essid, Miriam Bilac, Marine Chamoux, and Angelica Lim. Ue-hri: a new dataset for the study of user engagement in spontaneous human-robot interactions. In Proceedings of the 19th ACM International Conference on Multimodal Interaction, pages 464–472. ACM, 2017. [Wortwein] Torsten Wörtwein, Mathieu Chollet, Boris Schauerte, Louis-Philippe Morency, Rainer Stiefelhagen, and Stefan Scherer. 2015. Multimodal Public Speaking Performance Assessment. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (ICMI '15). Association for Computing Machinery, New York, NY, USA, 43–50. [Chollet21] Chollet, M., Marsella, S., & Scherer, S. (2021). Training public speaking with virtual social interactions: effectiveness of real-time feedback and delayed feedback. Journal on Multimodal User Interfaces, 1-13.
Other references: [TPT] https://www.telecom-paristech.fr/eng/ [IMTA] https://www.imt-atlantique.fr/fr [SSA] http://www.tsi.telecom-paristech.fr/ssa/# [PACCE] https://www.ls2n.fr/equipe/pacce/ [Clavel] https://clavel.wp.imt.fr/publications/ [Chollet] https://matchollet.github.io/ [Biancardi] https://sites.google.com/view/beatricebiancardi -Rasipuram, Sowmya, and Dinesh Babu Jayagopi. 'Automatic multimodal assessment of soft skills in social interactions: a review.' Multimedia Tools and Applications (2020): 1-24. -Sharma, Rahul, Tanaya Guha, and Gaurav Sharma. 'Multichannel attention network for analyzing visual behavior in public speaking.' 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. -Acharyya, R., Das, S., Chattoraj, A., & Tanveer, M. I. (2020, April). FairyTED: A Fair Rating Predictor for TED Talk Data. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 338-345).
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6-10 | (2021-12-18) Master or PhD internships Hi, You are in a Master or PhD program (in NLP or Speech proc.) and want to do an internship in 2022 co-supervised by
and
?
This offer is for you ! https://tinyurl.com/intern-nle-sb (You can apply online from the Web link)
—————
Joint ASR and Repunctuation for Better Machine and Human Readable Transcripts
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6-11 | (2021-12-19) 2 research engineer positions ALAIA, IRIT, Toulouse, France Afin de renforcer son équipe, le laboratoire Commun ALAIA, destiné à l'Apprentissage des langues Assisté par Intelligence Artificielle, propose deux postes d'ingénieurs de recherche (12 mois). ALAIA est centré sur l'expression et la compréhension orale d'une langue étrangère cible (L2). En collaboration avec ses deux partenaires, académique (IRIT) et industriel (Archean Technologie) ainsi que des experts en didactiques de langues, les missions consisteront à concevoir, développer et intégrer des services innovants basés sur l'analyse des productions des apprenants L2, la détection et la caractérisation d'erreurs allant du niveau phonétique au niveau linguistique. Elles seront affinées en fonction du profil des personnes recrutées. Les compétences attendues portent sur le traitement automatique de la parole et du langage ainsi que les méthodes de machine learning.
Les candidatures sont à adresser à Isabelle Ferrané (isabelle.ferrane@irit.fr) et Lionel Fontan (lfontan@archean.tech). N'hésitez pas à nous contacter pour de plus amples informations.
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6-12 | (2021-12-26) Research associate and postdoc at Heriot-Watt University, Edinburgh, UK 1) Research Associate in Safe Conversational AI(re-advertising) Closing date: 9th January 2022
?
We seek a candidate with experience in neural approaches to natural language generation, or closely related fields, including Vision + Language tasks.
Applicants interested in social computing tasks, such as online abuse detection and mitigation, as well as interdisciplinary candidates with a wider interest in ethical and social implications of NLP are also encouraged to apply.
The opportunity:
This is an exciting opportunity to work with a team developing safer AI methods bringing together AI researchers and researchers working on formal verification methods to researchers working on computational law. You will contribute your insight and experience into researching and developing deep learning methods for Conversational AI and closely related areas.
The project is led by Heriot-Watt University and in cooperation with the University of Edinburgh and Strathclyde, see https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/T026952/1
2) Postdoctoral Research Assistant in children's perceptions of technology
Closing date: 6th January 2022
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We are looking for a creative and self-motivated researcher to investigate children?s knowledge and perceptions of conversational agents such as Alexa. The position is located at the University of Edinburgh's Moray House School of Education.
The opportunity:
This is an exciting opportunity to work with an interdisciplinary team of computer scientists and social psychologists at three Scottish universities on a project to address gender bias in conversational agents. You will contribute your insight and experience into researching technology with and for children to the team.
The project is led by Heriot-Watt University and in cooperation with the University of Edinburgh and Strathclyde, see https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/T024771/1
For any enquiries, please get in touch!
Prof. Verena Rieser
Heriot-Watt University, Edinburgh
https://sites.google.com/site/verenateresarieser/
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6-13 | (2022-01-10) Conference coordinator, ISCA. The International Speech Communication (ISCA) [www.isca-speech.org) seeks application for a :
Conference Coordinator (f/m/d)
For a limited-term contract with 18h/week, with the perspective of extension towards an unlimited-term contract.
The International Speech Communication Association (ISCA) is a scientific non-profit organization according to the French law 1901. The purpose of the association is to promote, in an international world-wide context, activities and exchanges in all fields related to speech communication science and technology. The association is aimed at all persons and institutions interested in fundamental research and technological development that aims at describing, explaining and reproducing the various aspects of human communication by speech, e.g. phonetics, linguistics, computer speech recognition and synthesis, speech compression, speaker recognition, aids to medical diagnosis of voice pathologies.
One of the core activities of ISCA is to ensure the continuous organization of its flagship conference, Interspeech. The conference is organized each year in a different country by a different team; it typically attracts 1500 or more participants from all over the world. The role of this newly-created position of conference coordinator is to ensure a smooth organization of the conference over the years, according to well-established standards, and taking into account the aims ISCA has with the conference.
The role requires – amongst others – to take over the lead for the following activities:
Required competences:
We are looking for a self-motivated person who is enthusiastic about the organization of international scientific events, and has excellent organizational and communication skills (mostly in English). The person does not need to have a scientific background in speech communication and technology, but should be able to understand the scientific background, as well as the aims ISCA has with the organization of Interspeech conferences. A proven expertise in the organization of large-scale events is a must, and of scientific events is a plus.
The job can be carried out remotely from any location. A flexible allocation of time over the year is required, depending on the status of preparations (the conference is typically organized in September, and there is an expected increase in activity from March to September). The willingness to physically attend preparatory meetings and the conference is required.
Deadline : 15 March 2022.
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6-14 | (2022-01-28) ASSISTANT OR ASSOCIATE PROFESSOR IN SPEECH AND LANGUAGE TECHNOLOGY (tenure track) at Aalto University, Finland Aalto opens a call for an assistant or associate professor in speech and language technology.
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6-15 | (2022-02-01) Two post-docs at ADAPT, Dublin, Irland
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6-16 | (2022-02-02) Professeur(-e), Sorbonne Université, Paris, France Un poste de Professeure / Professeur des Universités en Intelligence artificielle : théorie et applications, est à pourvoir à Sorbonne Université avec une affection recherche dans un des laboratoires : ISIR, LIB, LIMICS ou LIP6.
Professeure / Professeur des Universités
Section 27 – Informatique Profil : Intelligence artificielle : théorie et applications Date limite des candidatures au poste : le 04 mars 2022 à 16h La personne recrutée contribuera significativement aux enseignements de Licence d’informatique dont les besoins couvrent l’ensemble de la discipline (algorithmique, programmation notamment objet, concurrente, fonctionnelle, web, mathématiques discrètes, structures de données, système, architecture, réseaux, compilation, bases de données, etc.) ainsi qu’au master d’informatique, en particulier pour les parcours ANDROIDE, BIM ou DAC. Recherche : Le poste est ouvert à tous les domaines de l’IA et de ses applications. La personne retenue intégrera l’un des laboratoires : ISIR, LIB, LIMICS ou LIP6 selon ses thématiques de recherche, et/ou de projets impliquant plusieurs laboratoires d’accueil au sein de SCAI (Sorbonne Center for Artificial Intelligence). La professeure ou le professeur devra être capable de coordonner des programmes collaboratifs nationaux et internationaux. La participation de la candidate ou du candidat, dans le passé, à des projets multidisciplinaires sera appréciée. Lien vers la fiche de poste : https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/ListesPostesPublies/FIDIS/0755890V/FOPC_0755890V_391.pdf Lien vers le site de recrutement : https://recrutement.sorbonne-universite.fr/fr/personnels-enseignants-chercheurs-enseignants-chercheurs/enseignants-chercheurs/recrutement-2022-des-enseignantes-chercheuses-et-enseignants-chercheurs.html
Contact à l’ISIR : Guillaume MOREL, directeur de l’ISIR : guillaume.morel(at)sorbonne-universite.fr En vous remerciant d’avance pour votre aide dans le partage de cette offre.
Bien cordialement,
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6-17 | (2022-02-11) 2 research fellowship grant for collaboration in research activities, Kore University of Enna - Enna (Sicily), Italy A public selection procedure is called, based on qualifications and interview, for the assignment of n. 2 research fellowship grant for collaboration in research activities
Project main aim: Multidisciplinary Research on AI for Health. Location: Kore University of Enna - Enna (Sicily), Italy Funding Programme: Research Projects of National Relevance - PRIN 2017
Description: The project focuses on idiopathic Parkinson’s disease dysarthric speech, produced by speakers of two varieties of Italian that show different segmental (consonantal, vocalic) and prosodic characteristics. The project as a whole aims at: identifying phonetic features that impact on speech intelligibility and accuracy, separating variability due to dysarthria from features due to sociolinguistic variation, and developing perspectives and tools for clinical practice that take variation into account.
Duration: 12 months
Link to apply: https://unikore.it/index.php/it/contratti-di-ricerca/item/41282-d-p-n-33-2022-2-assegni-di-ricerca-presso-l-universita-degli-studi-di-enna-kore
Further information contact Prof. Sabato Marco Siniscalchi, E-mail: marco.siniscalchi-at-unikore.it
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6-18 | (2022-02-08) PhD position at Delft University, The NetherlandsJob descriptionOne of the most pressing matters that holds back robots from taking on more tasks and reach a widespread deployment in society is their limited ability to understand human communication and take situation-appropriate actions. This PhD positions is dedicated to addressing this gap by developing the underlying data-driven models that enable a robot to engage with humans in a socially aware manner. This position is specifically targeted at the development of an argumentative dialogue system for human-robot interaction. The PhD candidate will explore how to fuse multimodal behaviour to infer a person's perspective. The candidate will use, and further develop, reinforcement learning techniques in order to drive the robot's argumentative strategy for deliberating topics of current social importance such as global warming or vaccination. The ideal candidate will have a keen interest in speech technology and reinforcement learning. He or she has strong interactive system background will design and run the experiments to evaluate the created hybrid-AI models through human-robot interaction. Topics of interest: 1) long-term human-robot interaction 2) affective computing 2) NLP&argument-mining Requirements
Click here to apply:
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6-19 | (2022-02-17) Postdoctoral position at INRIA, Bordeaux, France Postdoctoral position in Speech Processing at INRIA, Bordeaux, France
Title: Glottal source inverse filtering for the analysis and classification of pathological speech Keywords: Pathological speech processing, Glottal source estimation, Inverse filtering, Machine learning, Parkinsonian disorders, Respiratory diseases Contact and Supervisor: Khalid Daoudi (khalid.daoudi@inria.fr) INRIA team: GEOSTAT (geostat.bordeaux.inria.fr) Duration: 13 months (could be extended) Starting date: between 01/04/2022 and 01/06/2022 (depending on the candidate availability) Application : via https://recrutement.inria.fr/public/classic/en/offres/2022-04481 Salary: 2653€/month (before taxes, net salary 2132€) Profile: PhD thesis in signal/speech processing (or a solid post-thesis experience in the field) Required Knowledge and background: A solid knowledge in speech/signal processing; Basics of machine learning; Programming in Matlab and Python. Scientific research context During this century, there has been an ever increasing interest in the development of objective vocal biomarkers to assist in diagnosis and monitoring of neurodegenerative diseases and, recently, respiratory diseases because of the Covid-19 pandemic. The literature is now relatively rich in methods for objective analysis of dysarthria, a class of motor speech disorders [1], where most of the effort has been made on speech impaired by Parkinson’s disease. However, relatively few studies have addressed the challenging problem of discrimination between subgroups of Parkinsonian disorders which share similar clinical symptoms, particularly is early disease stages [2]. As for the analysis of speech impaired by respiratory diseases, the field is relatively new (with existing developments in very specialized areas) but is taking a great attention since the beginning of the pandemic. The speech production mechanism is essentially governed by five subsystems: respiratory, phonatory, articulatory, nasalic and prosodic. In the framework of pathological speech, the phonatory subsystem is the most studied one, usually using sustained phonation (prolonged vowels). Phonatory measurements are generally based on perturbations or/and cepstral features. Though these features are widely used and accepted, they are limited by the fact that the produced speech can be a product of some or all the other subsystems. The latter thus all contribute to the phonatory performance. An appealing way to bi-pass this problem is to try to extract the glottal source from speech in order to isolate the phonatory contribution. This framework is known as glottal source inverse filtering (GSIF) [3]. The primary objective of this proposal is to investigate GSIF methods in pathological speech impaired by dysarthria and respiratory deficit. The second objective is to use the resulting glottal parameterizations as inputs to basic machine learning algorithms in order to assist in the discrimination between subgroups of Parkinsonian disorders (Parkinson’s disease, Multiple-System Atrophy, Progressive Supranuclear Palsy) and in the monitoring of respiratory diseases (Covid-19, Asthma, COPD). Both objectives benefit from a rich dataset of speech and other biosignals recently collected in the framework of two clinical studies in partnership with university hospitals in Bordeaux and Toulouse (for Parkinsonian disorders) and in Paris (for respiratory diseases). Work description GSIF consists in building a model to filter out the effect of the vocal tract and lips radiation from the recorded speech signal. This difficult problem, even in the case of healthy speech, becomes more challenging in the case of pathological speech. We will first investigate time-domain methods for the parameterization of the glottal excitation using glottal opening and closure instants. This implies the development of a robust technique to estimate these critical time-instants from dysarthric speech. We will then explore the alternative approach of learning a parametric model of the entire glottal flow. Finally, we will investigate frequency-domain methods to determine relationships between different spectral measures and the glottal source. These algorithmic developments will be evaluated and validated using a rich set of biosignals obtained from patients with Parkinsonian disorders and from healthy controls. The biosignals are electroglottography and aerodynamic measurements of oral and nasal airflow as well as intra-oral and sub-glottic pressure. After dysarthric speech GIFS analysis, we will study the adaptation/generalization to speech impaired by respiratory deficits. The developments will be evaluated using manual annotations, by an expert phonetician, of speech signals obtained from patients with respiratory deficit and from healthy controls. The second aspect of the work consists in manipulating machine learning algorithms (LDA, logistic regression, decision trees, SVM…) using standard tools (such as Scikit-Learn). The goal here will be to study the discriminative power of the resulting speech features/measures and their complementarity with other features related to different speech subsystems. The ultimate goal being to conceive robust algorithms to assist, first, in the discrimination between Parkinsonian disorders and, second, in the monitoring of respiratory deficit. Work synergy - The postdoc will interact closely with an engineer who is developing an open-source software architecture dedicated to pathological speech processing. The validated algorithms will be implemented in this architecture by the engineer, under the co-supervision of the postdoc. - Giving the multidisciplinary nature of the proposal, the postdoc will interact with the clinicians participating in the two clinical studies. References: [1] J. Duffy. Motor Speech Disorders Substrates, Differential Diagnosis, and Management. Elsevier, 2013. [2] J. Rusz et al. Speech disorders reflect differing pathophysiology in Parkinson's disease, progressive supranuclear palsy and multiple system atrophy. Journal of Neurology, 262(4), 2015. [3] P. Alku. Glottal inverse filtering analysis of human voice production – A review of estimation and parameterization methods of the glottal excitation and their applications. Sadhana – Academy Proceedings in Engineering Sciences. Vol. 36, Part 5, pp. 623-650, 2011
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6-20 | (2022-02-15) Thèse CIFRE L3i La Rochelle-EasyChain
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