Special Issue of ACM Transactions on Accessible Computing (TACCESS) On

Speech and Language Interaction for Daily Assistive Technology

Guest Editors: François Portet, Frank Rudzicz, Jan Alexandersson, Heidi Christensen

Assistive technologies (AT) allow individuals with disabilities to do things that would otherwise be difficult or impossible. Many assistive technologies involve providing universal access, such as modifications to televisions or telephones to make them accessible to those with vision or hearing impairments. An important sub-discipline within this community is Augmentative and Alternative Communication (AAC), which has its focus on communication technologies for those with impairments that interfere with some aspect of human communication, including spoken or written modalities. Another important sub-discipline is Ambient Assisted Living (AAL) which facilitates independent living; these technologies break down the barriers faced by people with physical or cognitive impairments and support their relatives and caregivers. These technologies are expected to improve quality-of-life of users and promote independence, accessibility, learning, and social connectivity.

Speech and natural language processing (NLP) can be used in AT/AAC in a variety of ways including, improving the intelligibility of unintelligible speech, and providing communicative assistance for frail individuals or those with severe motor impairments. The range of applications and technologies in AAL that can rely on speech and NLP technologies is very large, and the number of individuals actively working within these research communities is growing, as evidenced by the successful INTERSPEECH 2013 satellite workshop on Speech and Language Processing for Assistive Technologies (SLPAT). In particular, one of the greatest challenges in AAL is to design smart spaces (e.g., at home, work, hospital) and intelligent companions that anticipate user needs and enable them to interact with and in their daily environment and provide ways to communicate with others. This technology can benefit each of visually-, physically-, speech- or cognitively- impaired persons.

Topics of interest for submission to this special issue include (but are not limited to):

• Speech, natural language and multimodal interfaces designed for people with physical or cognitive impairments
• Applications of speech and NLP technology (automatic speech recognition, synthesis, dialogue, natural language generation) for AT applications
• Novel modeling and machine learning approaches for AT applications
• Long-term adaptation of speech/NLP based AT system to user's change
• User studies, overview of speech/NLP technology for AT: understanding the user's needs and future speech and language based technologies.
• Understanding, modeling and recognition of aged or disordered speech
• Speech analysis and diagnosis: automatic recognition and detection of speech pathologies and speech capability loss
• Speech-based distress recognition
• Automated processing of symbol languages, sign language and nonverbal communication including translation systems.
• Text and audio processing for improved comprehension and intelligibility, e.g., sentence simplification or text-to-speech
• Evaluation methodology of systems and components in the lab and in the wild.
• Resources; corpora and annotation schemes
• Other topics in AAC, AAL, and AT

Submission process

Contributions must not have been previously published or be under consideration for publication elsewhere, although substantial extensions of conference or workshop papers will be considered. as long as they adhere to ACM's minimum standards regarding prior publication (http://www.acm.org/pubs/sim_submissions.html). Studies involving experimentations with real target users will be appreciated. All submissions have to be prepared according to the Guide for Authors as published in the Journal website at http://www.rit.edu/gccis/taccess/. 

Submissions should follow the journal's suggested writing format (http://www.gccis.rit.edu/taccess/authors.html) and should be submitted through Manuscript Central http://mc.manuscriptcentral.com/taccess , indicating that the paper is intended for the Special Issue. All papers will be subject to the peer review process and final decisions regarding publication will be based on this review.

Important dates:

◦              Extended deadline for full paper submission: 28th April 2014

◦              Response to authors: 30^th June 2014

◦              Revised submission deadline: 31^st August 2014

◦              Notification of acceptance: 31^st October 2014

◦              Final manuscripts due: 30^th November 2014

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Spatial audio is an area that has gained in popularity in the recent years. Audio reproduction setups evolved from the traditional two-channel loudspeaker setups towards multi-channel loudspeaker setups. Advances in acoustic signal processing even made it possible to create a surround sound listening experience using traditional stereo speakers and headphones. Finally, there has been an increased interest in creating different sound zones in the same acoustic space (also referred to as personal audio). At the same time, the computational capacity provided by mobile audio playback devices has increased significantly. These developments enable new possibilities for advanced audio signal processing, such that in the future we can record, transmit and reproduce spatial audio in ways that have not been possible before. In addition, there have been fundamental advances in our understanding of 3D audio.

Due to the increasing number of different formats and reproduction systems for spatial audio, ranging from headphones to 22.2 speaker systems, it is major challenge to ensure interoperability between formats and systems, and consistent delivery of high-quality spatial audio. Therefore, the MPEG committee is in the process of establishing new standards for 3D Audio Content Delivery.

The scope of this Special Issue on Spatial Audio is open to contributions ranging from the measurement and modeling of an acoustic space to reproduction and perception of spatial audio. While individual submissions may focus on any of the sub-topics listed below, papers describing a larger spatial audio signal processing systems will be considered as well.

We invite authors to address some of the following spatial audio aspects:

• Capture of Spatial Sound, use of different microphone arrays to record 3D sound fields
• Loudspeaker and Headphone Reproduction of Spatial Sound, including e.g. wave field synthesis, Ambisonics, arbitrary multi-channel loudspeaker setups, cross-talk cancellation systems, and personal audio systems
• Spatial Sound Processing including e.g. downmixing, upmixing, spatial sound enhancement, and reverberation effects
• Sound Source Localization and Room Geometry Estimation, advanced analysis of audio signals for reconstruction of the acoustic environment
• Room Acoustics Modeling covering all different modeling techniques ranging from computationally heavy wave-based techniques and geometrical acoustics to lightweight perceptually-based models.

Prospective authors should visit http://www.signalprocessingsociety.org/publications/periodicals/jstsp/ for information on paper submission. Manuscripts should be submitted at http://mc.manuscriptcentral.com/jstsp-ieee.

Manuscript Submission: July 1, 2014
First Review Due: October 15, 2014
Revised Manuscript: December 1, 2014
Second Review Due: February 1, 2015
Final Manuscript: March 1, 2015

Guest Editors:
Lauri Savioja, Aalto University, Finland (Lauri.Savioja@aalto.fi)
Akio Ando, University of Toyama, Japan (andio@eng.u-toyama.ac.jp)
Ramani Duraiswami, University of Maryland, USA (ramani@umiacs.umd.edu)
Emanuël Habets, Int. Audio Laboratories Erlangen, Germany (emanuel.habets@audiolabs-erlangen.de)
Sascha Spors, Universität Rostock, Germany (sascha.spors@uni-rostock.de)

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***** Journal of Natural Language Engineering - Special Issue on “Machine Translation Using Comparable Corpora” *****

CALL FOR PAPERS Statistical machine translation based on parallel corpora has been very successful. The major search engines' translation systems, which are used by millions of people, are primarily using this approach, and it has been possible to come up with new language pairs in a fraction of the time that would be required when using more traditional rule-based methods. In contrast, research on comparable corpora is still at an earlier stage. Comparable corpora can be defined as monolingual corpora covering roughly the same subject area in different languages but without being exact translations of each other. However, despite its tremendous success, the use of parallel corpora in MT has a number of drawbacks: 1) It has been shown that translated language is somewhat different from original language, for example Klebanov & Flor showed that 'associative texture' is lost in translation. 2) As they require translation, parallel corpora will always be a far scarcer resource than comparable corpora. This is a severe drawback for a number of reasons: a) Among the about 7000 world languages, of which 600 have a written form, the vast majority are of the 'low resource' type. b) The number of possible language pairs increases with the square of the number of languages. When using parallel corpora, one bitext is needed for each language pair. When using comparable corpora, one monolingual corpus per language suffices. c) For improved translation quality, translation systems specialized on particular genres and domains are desirable. But it is far more difficult to acquire appropriate parallel rather than comparable training corpora. d) As language evolves over time, the training corpora should be updated on a regular basis. Again, this is more difficult in the parallel case. For such reasons it would be a big step forward if it were possible to base statistical machine translation on comparable rather than on parallel corpora: The acquisition of training data would be far easier, and the unnatural 'translation bias' (source language shining through) within the training data could be avoided. But is there any evidence that this is possible? Motivation for using comparable corpora in MT research comes from a cognitive perspective: Experience tells that persons who have learned a second language completely independently from their mother tongue can nevertheless translate between the languages. That is, human performance shows that there must be a way to bridge the gap between languages which does not rely on parallel data. Using parallel data for MT is of course a nice shortcut. But avoiding this shortcut by doing MT based on comparable corpora may well be a key to a better understanding of human translation, and to better MT quality. Work on comparable corpora in the context of MT has been ongoing for almost 20 years. It has turned out that this is a very hard problem to solve, but as it is among the grand challenges in multilingual NLP, interest has steadily increased. Apart from the increase in publications this can be seen from the considerable number of research projects (such as ACCURAT and TTC) which are fully or partially devoted to MT using comparable corpora. Given also the success of the workshop series on “Building and Using Comparable Corpora“ (BUCC), which is now in its seventh year, and following the publication of a related book (http://www.springer.com/computer/ai/book/978-3-642-20127-1), we think that it is now time to devote a journal special issue to this field. It is meant to bundle the latest top class research, make it available to everybody working in the field, and at the same time give an overview on the state of the art to all interested researchers.

TOPICS OF INTEREST We solicit contributions including but not limited to the following topics: • Comparable corpora based MT systems (CCMTs) • Architectures for CCMTs • CCMTs for less-resourced languages • CCMTs for less-resourced domains • CCMTs dealing with morphologically rich languages • CCMTs for spoken translation • Applications of CCMTs • CCMT evaluation • Open source CCMT systems • Hybrid systems combining SMT and CCMT • Hybrid systems combining rule-based MT and CCMT • Enhancing phrase-based SMT using comparable corpora • Expanding phrase tables using comparable corpora • Comparable corpora based processing tools/kits for MT • Methods for mining comparable corpora from the Web • Applying Harris' distributional hypothesis to comparable corpora • Induction of morphological, grammatical, and translation rules from comparable corpora • Machine learning techniques using comparable corpora • Parallel corpora vs. pairs of non-parallel monolingual corpora • Extraction of parallel segments or paraphrases from comparable corpora • Extraction of bilingual and multilingual translations of single words and multi-word expressions, proper names, and named entities from comparable corpora

IMPORTANT DATES December 1, 2014: Paper submission deadline February 1, 2015: Notification May 1, 2015: Deadline for revised papers July 1, 2015: Final notification September 1, 2015: Final paper due

GUEST EDITORS Reinhard Rapp, Universities of Aix Marseille (France) and Mainz (Germany) Serge Sharoff, University of Leeds (UK) Pierre Zweigenbaum, LIMSI, CNRS (France) FURTHER INFORMATION Please use the following e-mail address to contact the guest editors: jnle.bucc (at) limsi (dot) fr Further details on paper submission will be made available in due course at the BUCC website: http://comparable.limsi.fr/bucc2014/bucc-introduction.html

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Manuscript due: Dec. 1, 2014

Journal: Speech Communication

Description:

Sparse and low-rank modeling aim to incorporate the low-dimensional
structures pertained to the geometry of the underlying problems to
achieve the optimal solution. These concepts have been proven to be very
effective for a wide range of applications at the intersection of
multiple fields, including machine learning, signal processing and
statistics.

In the context of audio and speech processing, and more particularly
multiparty communications in reverberant and overlapping conditions, the
integration of sparse and low-rank modeling concepts has lead to several
interesting new directions and promising results in speech communication
problems, ranging from denoising to deconvolution and from separation to
recognition. Several other exciting developments include sparse linear
prediction, missing data recovery, audio content analysis and
inpainting. Addressing such real applications is particularly
challenging due to the complex acoustic and speech characteristics, and
the need to develop new modeling strategies that meet the foundational
theoretical hypotheses. In addition, speech recognition performance
seems to degrade in these complex acoustic conditions, and thus research
in this direction is critical from both a theoretical and industry
perspective.

The goal of the proposed special issue is to consolidate the research in
these diverse fields in a coherent framework and overview the recent
advances and trends where sparse and low-rank modeling and applications
are converging to new fundamental and practical paradigms that could
also lead to the emergence of new speech technologies.



Topics of interest include:

* Manifold learning in speech processing: single and multi-microphone
speech enhancement
* Sparse modeling and low-rank modeling for separation and denoising
* Sparse regression and classification
* Sparse dimensionality reduction for feature extraction
* Structured sparsity models underlying audio and speech representation
* Auditory-inspired sparse modeling
* Sparse modeling and low-rank modeling for source localization
* Sparse representation and low-rank representation for reverberant
acoustic modeling
* Sparse data processing and modeling in low-resourced languages
* Applications in speech recognition, privacy-preserving speech
processing, speaker recognition and authentication, speaker diarization,
microphone array calibration, audio information retrieval, speech
synthesis and coding

Lead Guest Editors:

* Prof. Hervé Bourlard, herve.bourlard@idiap.ch
* Dr. Afsaneh Asaei, afsaneh.asaei@idiap.ch
* Dr. Tara N. Sainath, tsainath@us.ibm.com
* Prof. Sharon Gannot, sharon.gannot@biu.ac.il

For more information about this special issue, please visit:

http://si.eurasip.org/issues/36/advances-in-sparse-modeling-and-low-rank-modeling/
_______________________________________________

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Manuscript due: Dec. 15, 2014

Journal: EURASIP Journal on Audio, Speech, and Music Processing

Description:

Deep learning techniques have enjoyed enormous success in the speech and
language processing community over the past few years, beating previous
state-of-the-art approaches to acoustic modeling, language modeling, and
natural language processing. A common theme across different tasks is
that that the depth of the network allows useful representations to be
learned. For example, in acoustic modeling, the ability of deep
architectures to disentangle multiple factors of variation in the input,
such as various speaker-dependent effects on speech acoustics, has led
to excellent improvements in speech recognition performance on a wide
variety of tasks. In addition, in natural language processing and
language modeling tasks, integrating learned vector space models of
words, which perform smoothing and clustering based on semantic and
syntactic information contained in word contexts, with recurrent or
recursive architectures has led to significant advances.
 
We as a community should continue to understand what makes deep learning
successful for speech and language, and how further improvements can be
achieved. For example, just as deep networks made us re-think the input
feature representation pipeline used for speech recognition, we should
continue to push deep learning into other areas of the speech
recognition pipeline.
 
In addition, new architectures, such as convolutional neural networks
and recurrent networks using long short-term memory cells, have improved
performance, and we believe alternative architectures can improve
performance further. Secondly, optimization of large neural network
models remains a huge challenge, both because of computational cost and
amount of data, which could possibly be unsupervised.


Topics of interest include:

* New deep-learning architectures and algorithms
* Optimization strategies for deep learning
* Improved adaptation methods for deep learning
* Unsupervised and semi-supervised training for deep learning
* Novel applications of deep learning for speech and language tasks
* Theoretical and empirical understanding of deep learning for speech and 
 language
* Deep-learning toolkits and/or platforms for big data

Lead Guest Editor:

* Tara Sainath, Google Inc., USA

Guest Editors:

* Michiel Bacchiani, Google Inc., USA
* Hui Jiang, York University, Canada
* Brian Kingsbury, Thomas J. Watson Research Center, USA
* Hermann Ney, RWTH Aachen, Germany
* Frank Seide, Microsoft Research Asia, China
* Andrew Senior, Google Inc., USA

For more information about this special issue, please visit:

http://si.eurasip.org/issues/38/deep-learning-for-speech-and-language-processing/
_______________________________________________

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CALL FOR PAPERS  
DIALOGUE & DISCOURSE 
SPECIAL ISSUE ON DIALOGUE STATE TRACKING 

GUEST EDITORS 

Jason D. Williams, Microsoft Research
Antoine Raux, Lenovo
Matthew Henderson, Cambridge University

IMPORTANT DATES

Submission deadline:                    3 April 2015
Notification:                           26 June 2015
Final version of accepted papers due:   28 August 2015
Anticipated publication:                16 October 2015

INTRODUCTION 

Conversational systems are increasingly becoming a part of daily life, with examples including Apple's Siri, Google Now, 

Nuance Dragon Go, Xbox and Cortana from Microsoft, and numerous new entrants.  Many conversational systems include 

a dialogue state tracking function, which estimates relevant aspects of the interaction such as the user's goal, level of 

frustration, trust towards the system, etc, given all of the dialogue history so far.  For example, in a tourist information system,

 the dialogue state might indicate the type of business the user is searching for (pub, restaurant, coffee shop), their desired 

price range and type of food served.  Dialogue state tracking is difficult because automatic speech recognition (ASR) and 

spoken language understanding (SLU) errors are common, and can cause the system to misunderstand the user.  

At the same time, state tracking is crucial because the system relies on the estimated dialogue state to choose actions -- for 

example, which restaurants to suggest.

Most commercial systems use hand-crafted heuristics for state tracking, selecting the SLU result with the highest confidence score,

 and discarding alternatives. In contrast, statistical approaches consider many hypotheses for the dialogue state.  By exploiting 

correlations between turns and information from external data sources -- such as maps, knowledge bases, or models of past 

dialogues -- statistical approaches can overcome some SLU errors.

Although dialogue state tracking has been an active area of study for more than a decade, there has been a flurry of new work 

in the past 2 years.  This has been driven in part by the availability of common corpora and evaluation measures provided by a 

series of three research community challenge tasks called the Dialogue State Tracking Challenge.  With these resources, 

researchers are able to study dialogue state tracking without investing the time and effort required to build and operate a spoken 

dialogue system.  Shared resources also allow direct comparison of methods across research groups.  Results from the Dialogue 

State Tracking Challenge have been presented at special sessions in SIGDIAL 2013, SIGDIAL 2014, and IEEE SLT 2014.

TOPICS OF INTEREST

The aim of this special issue is to provide a forum for in-depth, journal-level work on dialogue state tracking.  

This issue welcomes papers covering any topic relevant to dialogue state tracking.  Specific examples include (but are not

 limited to):

- Algorithms for dialogue state tracking, including those based on machine learning or novel heuristics
- Adaptation and learning in dialogue state tracking, for example across domains, users, usage environments, etc.
- Analyses of dialogue state tracking methods, or analyses of characteristics of dialogue that affect dialogue state tracking
- Investigations of metrics used for dialogue state tracking, including the impact of dialogue state tracking on end-to-end 

dialogue systems
- Descriptions and analyses of resources for dialogue state tracking, including corpora 
- Applications of dialogue state tracking to new domains or new settings, such as multi-modal systems

Submissions should report on new work, or substantially expand on previously published work with additional experiments, 

analysis, or important detail.  Previously-published aspects may be included but should be clearly indicated.

RELEVANT RESOURCES

All data from the dialogue state tracking challenge series continues to be available for use, including the dialogue data itself,

 scripts for evaluation and baseline trackers, raw output from trackers entered in the challenges, and performance summaries. 

If your work is on dialogue state tracking for information-seeking dialogues and/or you think the data is appropriate, you are 

strongly encouraged to report results on these data, to enable comparison.

The dialogue state tracking challenge data is available here:

- Dialogue State Tracking Challenge 1:    http://research.microsoft.com/en-us/events/dstc/
- Dialogue State Tracking Challenge 2&3:  http://camdial.org/~mh521/dstc/

SUBMISSIONS

Papers should be submitted on the Dialogue & Discourse journal website, following instructions and formatting guidelines given

 there:

http://www.dialogue-and-discourse.org/submission.shtml

Submitted papers will be reviewed according to the Dialogue & Discourse reviewing criteria and appropriateness to the topic of 

the special issue.

CONTACT

Contact Jason Williams (jason.williams@microsoft.com) for further information about this call for papers.

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'Silencing the Echoes' - Processing of Reverberant Speech

Submission Instructions

asp.eurasipjournals.com

Manuscript Due

Feb. 1, 2015 (in 3 months, 3 weeks)

Description

The reverberation contained in speech signals captured by distant microphones reduces both the perceptual speech quality and the performance of automatic speech recognition (ASR) systems, hampering both human-human communication and human-machine interaction. Thus, for applications that depend on distant sound capture, processing of the speech signal to mitigate the reverberation problem is essential. Such applications include voice control of consumer products, e.g. smart phones, wearable devices, interactive TVs, and appliances in smart homes; hearing aids; voice communication with robots and avatars; automatic meeting transcription; speech recognition in call centers; automatic annotation of videos; speech-to-speech translation.

To enable researchers in the field of reverberant speech processing to carry out comprehensive evaluations of their methods based on a common database and common evaluation metrics, recently the Reverberant Voice Enhancement and Recognition Benchmark (REVERB) challenge has been organized. Inspired by the great interest induced by this challenge, we invite contributions on processing of reverberant speech signals both for signal enhancement to increase perceptual speech quality and for robust recognition of reverberant speech.

We strongly encourage the evaluation of the proposed approaches on the data provided by the REVERB challenge. Alternatively or additionally, well-documented and publicly available, databases can be used. We invite challenge participants to submit extended descriptions of their (enhanced) systems. Participation in the challenge is however not a prerequisite for submitting a contribution to this special issue.

Topics of interest include:

• Single-channel and multichannel speech dereverberation algorithms
• ASR robust to reverberation
• Interconnection of dereverberation and ASR systems
• Objective measures for evaluating the perceptual speech quality of dereverberated signals
• Estimation of reverberation parameters (e.g. reverberation time, direct-to-reverberation ratio)
• Dereverberation in dynamic scenarios
• Highly reverberant scenarios (reverberation times exceeding one second, e.g., concert halls, museums, terminal halls, houses of worship)
• System design for handling reverberant speech

---

Guest Editors

• Sharon Gannot, Faculty of Engineering, Bar-Ilan University, Israel
• Armin Sehr, Beuth Hochschule für Technik Berlin
• Emanuël Habets, Friedrich-Alexander-University of Erlangen-Nuremberg
• Keisuke Kinoshita, NTT Communication Science Laboratories
• Walter Kellermann, University Erlangen-Nuernberg, Germany
• Reinhold Haeb-Umbach, Department of Communications Engineering, University of Paderborn, Germany

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Manuscript due: Jan. 10, 2015

Journal: Speech Communication

Description:

Sparse and low-rank modeling aim to incorporate the low-dimensional
structures pertained to the geometry of the underlying problems to
achieve the optimal solution. These concepts have been proven to be very
effective for a wide range of applications at the intersection of
multiple fields, including machine learning, signal processing and
statistics.

In the context of audio and speech processing, and more particularly
multiparty communications in reverberant and overlapping conditions, the
integration of sparse and low-rank modeling concepts has lead to several
interesting new directions and promising results in speech communication
problems, ranging from denoising to deconvolution and from separation to
recognition. Several other exciting developments include sparse linear
prediction, missing data recovery, audio content analysis and
inpainting. Addressing such real applications is particularly
challenging due to the complex acoustic and speech characteristics, and
the need to develop new modeling strategies that meet the foundational
theoretical hypotheses. In addition, speech recognition performance
seems to degrade in these complex acoustic conditions, and thus research
in this direction is critical from both a theoretical and industry
perspective.

The goal of the proposed special issue is to consolidate the research in
these diverse fields in a coherent framework and overview the recent
advances and trends where sparse and low-rank modeling and applications
are converging to new fundamental and practical paradigms that could
also lead to the emergence of new speech technologies.


Topics of interest include:

* Manifold learning in speech processing: single and multi-microphone
speech enhancement
* Sparse modeling and low-rank modeling for separation and denoising
* Sparse regression and classification
* Sparse dimensionality reduction for feature extraction
* Structured sparsity models underlying audio and speech representation
* Auditory-inspired sparse modeling
* Sparse modeling and low-rank modeling for source localization
* Sparse representation and low-rank representation for reverberant
acoustic modeling
* Sparse data processing and modeling in low-resourced languages
* Applications in speech recognition, privacy-preserving speech
processing, speaker recognition and authentication, speaker diarization,
microphone array calibration, audio information retrieval, speech
synthesis and coding

Guest Editors:

* Prof. Hervé Bourlard, herve.bourlard@idiap.ch
* Dr. Afsaneh Asaei, afsaneh.asaei@idiap.ch
* Dr. Tara N. Sainath, tsainath@google.com
* Prof. Sharon Gannot, sharon.gannot@biu.ac.il

For more information about this special issue, please visit:

http://si.eurasip.org/issues/48/advances-in-sparse-modeling-and-low-rank-modeling/

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Special issue of Speech Communication on 'Phase-Aware Signal Processing in
Speech Communication'
http://si.eurasip.org/issues/46/phase-aware-signal-processing-in-speech/

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JAIR Special Track on Cross-language Algorithms and Applications


Track Editor
Lluís Màrquez, Qatar Computing Research Institute

Associate Track Editors
Marta R. Costa­jussà, Instituto Politécnico Nacional
Srinivas Bangalore, AT&T Labs-Research
Patrik Lambert, Universitat Pompeu Fabra
Elena Montiel-Ponsoda, Universidad Politécnica de Madrid


The Journal of Artificial Intelligence Research (JAIR) is pleased to
announce the launch of the Special Track on Cross-language Algorithms and
Applications. The core Artificial Intelligence technologies of speech and
natural language processing need to address the challenges of processing
multiple languages. While the first challenge of multilingualism is to
bridge the nomenclature gap for the same concepts, the next significant
challenge is to develop algorithms and applications that not only scale to
multiple languages but also leverage cross-lingual similarities for
improved natural language processing.

The goal of this special track is to serve as a home for the publication of
leading research on Cross-language Algorithms and Applications, focusing on
developing unified themes leading to the development of the science of
multi- and cross-lingualism.  Topics of interest include, but are not
limited to: efforts in the direction of multilingual transliteration;
multilingual document summarization; rapid prototyping of cross language
tools for low resource languages; and machine translation.

Articles published in the Cross-language Algorithms and Applications track
must meet the highest quality standards as measured by originality and
significance of the contribution and clarity of presentation. Papers will
be coordinated by the track editor and associate editors, and reviewed by
peer reviewers drawn from the JAIR Editorial Board and the larger
community. All articles should be submitted using the normal JAIR
submission process. Please indicate that the submission is intended for the
Special Track in the section 'Special Information for editors'.


For more information and submission instructions, please see:
http://www.jair.org/specialtrack-claa.html


Timetable

1st March 2015              Deadline for Submissions
1st June 2015                 Notification of Acceptance/Revision/Rejection
15th July 2015                Deadline for Re-submission of papers requiring revision
15th September 2015    Notification of Final Acceptance
1st November 2015        Final manuscript due

Contact: martaruizcostajussa@gmail.com

Submission Instructions: Use JAIR conventional submissions instructions
available at http://www.jair.org/submission_info.html

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Advances in Applied Acoustics

ticsCall for papers

Indexing

Scope

Get more information by contacting aiaa@seipub.org or visiting the website.

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 Second Call for Papers
Special issue of the journal Traitement automatique des Langues (TAL)

   COMPUTATIONAL LINGUISTICS AND COGNITIVE SCIENCES

Guest editors: T. Poibeau  (Lattice, France)
           & S. Vasishth (Univ. of Postdam, Germany)

Submission deadline : March 31st, 2015

Natural language understanding is one of the key problems both in
cognitive science and in computational linguistics. If both fields do
share common methods, the scientific goals are actually different.

In computational linguistics, the last decade has seen the rise of
quantitative and statistical methods leading to significative advances
in applied sub-fields such as speech recognition, machine translation or
information retrieval among others. This line of research has greatly
taken advantage of its ability to process very large amounts of data
that can be considered close to truly natural data.

Even if this new generation of models is sometimes inspired by
linguistic or psycholinguistic theories, it generally brings little or
no explanation to the broader question of understanding the natural
language competence.

On the other hand, language studies in cognitive science and in
psycholinguistics try to better understand the mechanisms underlying
natural language (including their neural basis) and more specifically
its acquisition by means of experimental investigations. One of the
novel aspects in several recent works is to use computational models
similar to those used in computational linguistics.

This special issue is dedicated to get a better picture of the
relationships between these two research environments. It specifically
raises two questions: 'what is the potential contribution of
computational-linguistics-inspired language modeling to cognitive
science' and conversely: 'what is the influence of cognitive science in
contemporary computational linguistics' ?

The call targets specifically contributions on actual applications of
methods from computational linguistics to the modelling of cognitive
phenomena and on the other hand on application of cognitive theories to
the computational modeling of language. The call addresses all aspects
of language modeling from speech to discourse.

Topics include, but are not limited to :

- Computational models of natural language acquisition, word clustering
and word segmentation
- Psycholinguistically motivated phonetic, phonological, morphological
syntactic, semantic, pragmatic studies of language
- Statistical and probabilistic modeling of factors encouraging one
production or interpretation over its competitors
- Models of language emergence, change and evolution
- Models of language processing and surprisal
- Experimental or corpus driven modeling and analysis of language

The call seeks for original papers gathering modeling aspects with
empirical or experimental aspects. It also targets theoretical or
methodological questions that would allow us to build bridges between
the two fields.

CONTACT

Thierry Poibeau     (thierry.poibeau@ens.fr)
Shravan Vasishth    (vasishth@rz.uni-potsdam.de)

SCIENTIFIC COMMITTEE (to be completed)

F. Alario (LPC, Univ. Aix-Marseille, France)
A. Alishahi (Tilburg University, The Netherlands)
M. Amblard (LORIA, Univ. de Lorraine, France)
P. Blache (LPL, Aix-en-Provence, France)
A. Christophe (LSCP, Paris, France)
S. Colonna (SFL, Univ. Paris 8, France)
I. Dautriche (LSCP, Paris, France)
E. Dupoux (LSCP, Paris, France)
T. François (Cental, UCL, Belgium)
B. Gaume (CLLE-ERSS, Toulouse, France)
J. Ginzburg (CLILLAC, Univ. Paris 7, France)
M. Grant (McGill University, Canada)
J. Hale (Cornell University, USA)
B. Hemforth (LLF, Univ. Paris 7, Paris)
P. Logacev (Potsdam University, Germany)
F. Pellegrino (DDL, Lyon, France)
D. Reitter (Penn State University, USA)
E. Shutova (U. Berkeley, USA)
J. Thuilier (ALC, Univ. Rennes 2, France)
A. Villavicencio (UFRGS, Brazil)
T. von der Malsburg (UC San Diego, USA)
M. Zock (LIF, Marseille, France)

THE JOURNAL

TAL (Traitement Automatique des Langues / Natural Language Processing)
is a forty year old international journal published by ATALA (French
Association for Natural Language Processing) with the support of CNRS
(National Centre for Scientific Research). It has moved to an electronic
mode of publication, with printing on demand. This affects in no way its
reviewing and selection process.

IMPORTANT DATES 

- submission of contributions:   March 31st 2015
- first notification to authors: June 15th 2015
- revised versions:              July 30th 2015
- final decision:                September 15th 2015
- final versions:                October 15th 2015
- publication:                   Winter 2015

LANGUAGE

Manuscripts may be submitted in English or French. French-speaking
authors are requested to submit their contributions in French.

PAPER SUBMISSION

Papers must describe original, completed, and unpublished work. Each
submission will be reviewed by two programme committee members.

Papers must be submitted on Sciencesconf platform
     http://tal-55-3.sciencesconf.org/
To upload a paper, you will need to have an account on the
sciencesconf platform.

*** TAL has adopted double-blind review. The submitted papers should
  be anonymized. ***

Accepted papers will be maximum 25 pages long in PDF. Style sheets are
available for download on the Web site of the TAL journal
(http://www.atala.org/IMG/zip/tal-style.zip)

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