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ISCApad #195 |
| Friday, September 12, 2014 by Chris Wellekens |
7 Journals
| 7-1 | Special Issue on 'Signal Processing Techniques for Assisted Listening' of IEEE SIGNAL PROCESSING MAGAZINE IEEE Signal Processing Society Special Issue IEEE SIGNAL PROCESSING MAGAZINE Special Issue on 'Signal Processing Techniques for Assisted Listening'
Aims and Scope This special issue focuses on technical challenges of assisted listening from a signal processing perspective. Prospective authors are invited to contribute tutorial and survey articles that articulate signal processing methodologies which are critical for applying assisted listening techniques to mobile phones and other communication devices. Of particular interest is the role of signal processing in combining multimedia content, voice communication and voice pick-up in various real-world settings. Tutorial and survey papers are solicited on advances in signal processing that particularly apply to the following applications:
These can include the following suggested topics as they relate to the above applications:
Submission Process Important Dates: Expected publication date for this special issue is March 2015.
Guest Editors
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| 7-2 | CSL Special Issue on Speech and Language for Interactive RobotsCSL Special Issue on Speech and Language for Interactive RobotsAims and Scope
Speech-based communication with robots faces important challenges for their application in real world scenarios. In contrast to conventional interactive systems, a talking robot always needs to take its physical environment into account when communicating with users. This is typically unstructured, dynamic and noisy and raises important challenges. The objective of this special issue is to highlight research that applies speech and language processing to robots that interact with people through speech as the main modality of interaction. For example, a robot may need to communicate with users via distant speech recognition and understanding with constantly changing degrees of noise. Alternatively, the robot may coordinate its verbal turn-taking behaviour with its non-verbal one such as generating speech and gestures at the same time. Speech and language technologies have the potential of equipping robots so that they can interact more naturally with humans, but their effectiveness remains to be demonstrated. This special issue aims to help fill this gap. The topics listed below indicate the range of work that is relevant to this special issue, where each article will normally represent one or more topics. In case of doubt about the relevance of your topic, please contact the special issue associate editors. Topics
Special Issue Associate Editors Heriberto Cuayáhuitl, Heriot-Watt University, UK (contact: hc213@hw.ac.uk) Paper Submission All manuscripts and any supplementary materials will be submitted through Elsevier Editorial System at http://ees.elsevier.com/csl/. A detailed submission guideline is available as “Guide to Authors” at here. Please select “SI: SL4IR” as Article Type when submitting the manuscripts. For further details or a more in-depth discussion about topics or submission, please contact Guest Editors. Dates 23 May 2014: Submission of manuscripts
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| 7-3 | CSL Special Issue on Speech Production in Speech Technologies DEADLINE EXT.CSL Special Issue on Speech Production in Speech TechnologiesThe use of speech production knowledge and data to enhance speech recognition and other technologies is being actively pursued by a number of widely dispersed research groups using different approaches. The types of speech production information may include continuous articulatory measurements or discrete-valued articulatory or phonological features. These quantities might be directly measured, manually labeled, or unobserved but considered to be latent variables in a statistical model. Applications of production-based ideas include improved speech recognition, silent speech interface, language training tools, and clinical models of speech disorders. The goal of this special issue is to highlight the current state of research efforts that use speech production data or knowledge. The range of data, techniques, and applications currently being explored is growing, and is also benefiting from new ideas in machine learning, making this a particularly exciting time for this research. A recent workshop, the 2013 Interspeech satellite workshop on Speech Production in Automatic Speech Recognition (SPASR), as well as the special session on Articulatory Data Acquisition and Processing, brought together a number of researchers in this area. This special issue will expand on the topics included in these events and beyond. Submissions focusing on research in this area are solicited. Topics of interest include, but are not limited to: - The collection, labeling, and use of speech production data - Acoustic-to-articulatory inversion - Speech production models in speech recognition, synthesis, voice conversion, and other technologies - Silent speech interfaces - Atypical speech production and pathology - Articulatory phonology and models of speech production Submission procedure Prospective authors should follow the regular guidelines of the Computer Speech and Language Journal for electronic submission (http://ees.elsevier.com/csl). During submission authors must select 'SI: Speech Production in ST' as Article Type. Review procedure All manuscripts will be submitted through the editorial submission system and will be reviewed by at least 3 experts. Schedule:
Eric Fosler-Lussier, Ohio State U., fosler@cse.ohio-state.edu Mark Hasegawa-Johnson, U. Illinois at Urbana-Champaign, jhasegaw@uiuc.edu Karen Livescu, TTI-Chicago, klivescu@ttic.edu Frank Rudzicz, U. Toronto, frank@cs.toronto.edu
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| 7-4 | Special Issue of ACM Transactions on Accessible Computing (TACCESS) on Speech and Language Interaction for Daily Assistive Technology 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):
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: 30th June 2014 ◦ Revised submission deadline: 31st August 2014 ◦ Notification of acceptance: 31st October 2014 ◦ Final manuscripts due: 30th November 2014
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| 7-5 | Revue TAL: numéro spécial sur le traitement automatique du langage parlé (updated)
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| 7-6 | IEEE Journal of Selected Topics in Signal Processing: Special Issue on Spatial Audio IEEE Journal of Selected Topics in Signal Processing Special Issue on Spatial Audio
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:
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 Guest Editors:
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| 7-7 | Journal of Natural Language Engineering - Special Issue on “Machine Translation Using Comparable Corpora”
***** 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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| 7-8 | IEEE/ZACM Trans. ASLP: special issue on continuous space and related methods in natural language processing
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| 7-9 | CfP Special issue Speech Communication on Advances in Sparse Modeling and Low-rank Modeling for Speech ProcessingManuscript 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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| 7-10 | [Special Issue] Deep Learning for Speech and Language Processing Applications, EURASIPManuscript 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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| 7-11 | SPECIAL ISSUE ON DIALOGUE STATE TRACKINGCALL 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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