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ISCApad #163 |
| Wednesday, January 11, 2012 by Chris Wellekens |
7 Journals
| 7-1 | Special issue Signal Processing : LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION The journal Signal Processing published by Elsevier is issuing a call for a special issue on latent variable models and source separation. Papers dealing with multi-talker ASR and noise-robust ASR using source separation techniques are highly welcome.
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| 7-2 | IEEE Signal Processing Magazine: Special Issue on Fundamental Technologies in Modern Speech Recognition IEEE Signal Processing Magazine Special Issue on Fundamental Technologies in Modern Speech Recognition
Guest Editors: Sadaoki Furui Tokyo Institute of Technology, Tokyo, Japan (furui@cs.titech.ac.jp) Li Deng Microsoft Research, Redmond, USA (deng@microsoft.com) Mark Gales University of Cambridge, Cambridge, UK (mjfg@eng.cam.ac.uk) Hermann Ney RWTH Aachen University, Aachen, Germany (ney@cs.rwth-aachen.de) Keiichi Tokuda Nagoya Institute of Technology, Nagoya, Japan (tokuda@nitech.ac.jp) Recently, various statistical techniques that form the basis of fundamental technologies underlying today’s automatic speech recognition (ASR) research and applications have attracted new attentions. These techniques have significantly contributed to progress in ASR, including speaker recognition, and their various applications. The purpose of this special issue is to bring together leading experts from various disciplines to explore the impact of statistical approaches on ASR. The special issue will provide a comprehensive overview of recent developments and open problems. This Call for Papers invites researchers to contribute articles that have a broad appeal to the signal processing community. Such an article could be for example a tutorial of the fundamentals or a presentation of a state-of-the-art method. Examples of the topics that could be addressed in the article include, but are not limited to: * Supervised, unsupervised, and lightly supervised training/adaptation * Speaker-adaptive and noise-adaptive training * Discriminative training * Large-margin based methods * Model complexity optimization * Dynamic Bayesian networks for various levels of speech modeling and decoding * Deep belief networks and related deep learning techniques * Sparse coding for speech feature extraction and modeling * Feature parameter compensation/normalization * Acoustic factorization * Conditional random fields (CRF) for modeling and decoding * Acoustic source separation by PCA and ICA * De-reverberation * Rapid language adaptation for multilingual speech recognition * Weighted-finite-state-transducer (WFST) based decoding * Uncertainty decoding * Speaker recognition, especially text-independent speaker verification * Statistical framework for human-computer dialogue modeling * Automatic speech summarization and information extraction
Submission Procedure: Prospective authors should submit their white papers to the web submission system at http://mc.manuscriptcentral.com/spmag-ieee.
Schedule: * White paper due: October 1, 2011 * Invitation notification: November 1, 2011 * Manuscript due: February 1, 2012 * Acceptance notification: April 1, 2012 * Final manuscript due: May 15, 2012 * Publication date: September 15, 2012
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| 7-3 | CSL Special issue on SPEECH SEPARATION AND RECOGNITION IN MULTISOURCE ENVIRONMENTS COMPUTER SPEECH AND LANGUAGE
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| 7-4 | CfP Speech communication: Special issue on Processing Under-Resourced Languages
Correction: it is a special issue of Speech communication and not of Signal Processing as previously announced. Call for Papers Special Issue on Processing Under-Resourced Languages
The creation of language and acoustic resources, for any given spoken language, is typically a costly task. For example, a large amount of time and money is required to properly create annotated speech corpora for automatic speech recognition (ASR), domain-specific text corpora for language modeling (LM), etc. The development of speech technologies (ASR, Text-to-Speech) for the already high-resourced languages (such as English, French or Mandarin, for example) is less constrained by this issue and, consequently, high-performance commercial systems are already on the market. On the other hand, for under-resourced languages, the above issue is typically the main obstacle.
Given this, the scientific community’s concern with porting, adapting, or creating language and acoustic resources or even models for low-resourced languages has been growing recently and several algorithms and methods of adaptation have been proposed and experimented with. In the mean time, workshops and special sessions have been organized on this domain.
This special issue focuses on research and development of new tools based on speech technologies for less-resourced national languages, mainly, used in the following large geographical regions: Eastern Europe, South and Southeast Asia, West Asia, North Africa, Sub-Saharan Africa, South and Central America, Oceania. The special issue is open to present problems and peculiarities of targeted languages in application to spoken language technologies, including automatic speech recognition, text-to-speech, speech-to-speech translation, spoken dialogue systems in an internationalized context. When developing speech-based technologies researchers are faced with many new problems from lack of audio databases and linguistic resources (lexicons, grammars, text collections), to inefficiency of existing methods for language and acoustical modeling, and limited infrastructure for the creation of relevant resources. They often have to deal with novel linguistic phenomena that are poorly studied or researched from a speech technology perspective (for instance, clicks in southern African languages, tone in many languages of the world, language switching in multilingual systems, rich morphology, etc).
Well-written papers on speech technologies for targeted languages are encouraged, and papers describing original results (theoretical and/or experimental) obtained for under-resourced languages, but important for well-elaborated languages too, are invited as well. Good papers from any countries and any authors may be accepted if they present new speech studies concerning the languages of interest of the special issue. Submissions from countries where issues related to under-resourced languages are a practical reality, are strongly encouraged for this special issue.
Important Dates: Submission deadline: 1st August 2012 Notification of acceptance: 1st February 2013 Final manuscript due: April 2013 Tentative publication date: Summer 2013
Editors Etienne Barnard North-West University, South Africa Laurent Besacier Laboratory of Informatics of Grenoble, France Alexey Karpov SPIIRAS, Saint-Petersburg, Russia Tanja Schultz University of Karlsruhe, Germany
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| 7-5 | CfP Special issue ACM Transactions on Speech and Language Processing on Multiword Expressions Call for Papers ACM Transactions on Speech and Language Processing Special Issue on Multiword Expressions: from Theory to Practice and Use multiword.sf.net/tslp2011si Deadline for Submissions: May, 15th, 2012 Multiword expressions (MWEs) range over linguistic constructions like idioms (a frog in the throat, kill some time), fixed phrases (per se, by and large, rock'n roll), noun compounds (traffic light, cable car), compound verbs (draw a conclusion, go by [a name]), etc. While easily mastered by native speakers, their interpretation poses a major challenge for computational systems, due to their flexible and heterogeneous nature. Surprisingly enough, MWEs are not nearly as frequent in NLP resources (dictionaries, grammars) as they are in real-word text, where they have been reported to account for half of the entries in the lexicon of a speaker and over 70% of the terms in a domain. Thus, MWEs are a key issue and a current weakness for tasks like natural language parsing and generation, as well as real-life applications such as machine translation. In spite of several proposals for MWE representation ranging along the continuum from words-with-spaces to compositional approaches connecting lexicon and grammar, to date, it remains unclear how MWEs should be represented in electronic dictionaries, thesauri and grammars. New methodologies that take into account the type of MWE and its properties are needed for efficiently handling manually and/or automatically acquired expressions in NLP systems. Moreover, we also need strategies to represent deep attributes and semantic properties for these multiword entries. While there is no unique definition or classification of MWEs, most researchers agree on some major classes such as named entities, collocations, multiword terminology and verbal expressions. These, though, are very heterogeneous in terms of syntactic and semantic properties, and should thus be treated differently by applications. Type-dependent analyses could shed some light on the best methodologies to integrate MWE knowledge in our analysis and generation systems. Evaluation is also a crucial aspect for MWE research. Various evaluation techniques have been proposed, from manual inspection of top-n candidates to classic precision/recall measures. The use of tools and datasets freely available on the MWE community website (multiword.sf.net/PHITE.php?sitesig=FILES) is encouraged when evaluating MWE treatment. However, application-oriented techniques are needed to give a clear indication of whether the acquired MWEs are really useful. Research on the impact of MWE handling in applications such as parsing, generation, information extraction, machine translation, summarization can help to answer these questions. We call for papers that present research on theoretical and practical aspects of the computational treatment of MWEs, specifically focusing on MWEs in applications such as machine translation, information retrieval and question answering. We also strongly encourage submissions on processing MWEs in the language of social media and micro-blogs. The focus of the special issue, thus, includes, but is not limited to the following topics: * MWE treatment in applications such as the ones mentioned above; * Lexical representation of MWEs in dictionaries and grammars; * Corpus-based identification and extraction of MWEs; * Application-oriented evaluation of MWE treatment; * Type-dependent analysis of MWEs; * Multilingual applications (e.g. machine translation, bilingual dictionaries); * Parsing and generation of MWEs, especially, processing of MWEs in the language of social media and micro-blogs; * MWEs and user interaction; * MWEs in linguistic theories like HPSG, LFG and minimalism and their contribution to applications; * Relevance of research on first and second language acquisition of MWEs for applications; * Crosslinguistic studies on MWEs. Submission Procedure Authors should follow the ACM TSLP manuscript preparation guidelines described on the journal web site http://tslp.acm.org and submit an electronic copy of their complete manuscript through the journal manuscript submission site http://mc.manuscriptcentral.com/acm/tslp. Authors are required to specify that their submission is intended for this special issue by including on the first page of the manuscript and in the field 'Author's Cover Letter' the note 'Submitted for the special issue on Multiword Expressions'. Schedule Submission deadline: May, 15th, 2012 Notification of acceptance: September, 15th , 2012 Final manuscript due: November, 31st, 2012 Program Committee * Iñaki Alegria, University of the Basque Country (Spain) * Dimitra Anastasiou, University of Bremen (Germany) * Eleftherios Avramidis, DFKI GmbH (Germany) * Timothy Baldwin, University of Melbourne (Australia) * Francis Bond, Nanyang Technological University (Singapore) * Aoife Cahill, ETS (USA) * Helena Caseli, Federal University of Sao Carlos (Brazil) * Yu Tracy Chen, DFKI GmbH (Germany) * Paul Cook, University of Melbourne (Australia) * Ann Copestake, University of Cambridge (UK) * Béatrice Daille, Nantes University (France) * Gaël Dias, University of Caen Basse-Normandie (France) * Stefan Evert, University of Darmstadt (Germany) * Roxana Girju, University of Illinois at Urbana-Champaign (USA) * Chikara Hashimoto, National Institute of Information and Communications Technology (Japan) * Kyo Kageura, University of Tokyo (Japan) * Martin Kay, Stanford University and Saarland University (USA & Germany) * Su Nam Kim, University of Melbourne (Australia) * Dietrich Klakow, Saarland University (Germany) * Philipp Koehn, University of Edinburgh (UK) * Ioannis Korkontzelos, University of Manchester (UK) * Brigitte Krenn, Austrian Research Institute for Artificial Intelligence (Austria) * Evita Linardaki, Hellenic Open University (Greece) * Takuya Matsuzaki, Tsujii Lab, University of Tokyo (Japan) * Yusuke Miyao, Japan National Institute of Informatics (NII) (Japan) * Preslav Nakov , Qatar Foundation (Qatar) * Gertjan van Noord, University of Groningen (The Netherlands) * Diarmuid Ó Séaghdha, University of Cambridge (UK) * Jan Odijk, University of Utrecht (The Netherlands) * Pavel Pecina, Charles University (Czech Republic) * Scott Piao, Lancaster University (UK) * Thierry Poibeau, CNRS and École Normale Supérieure (France) * Maja Popovic, DFKI GmbH (Germany) * Ivan Sag, Stanford University (USA) * Agata Savary, Université François Rabelais Tours (France) * Violeta Seretan, University of Geneva (Switzerland) * Ekaterina Shutova, University of Cambridge (UK) * Joaquim Ferreira da Silva, New University of Lisbon (Portugal) * Lucia Specia, University of Wolverhampton (UK) * Sara Stymne, Linköping University (Sweden) * Stan Szpakowicz, University of Ottawa (Canada) * Beata Trawinski, University of Vienna (Austria) * Kyioko Uchiyama, National Institute of Informatics (Japan) * Ruben Urizar, University of the Basque Country (Spain) * Tony Veale, University College Dublin (Ireland) * David Vilar, DFKI GmbH (Germany) * Begoña Villada Moirón, RightNow (The Netherlands) * Tom Wasow, Stanford University (USA) * Shuly Wintner, University of Haifa (Israel) * Yi Zhang, DFKI GmbH and Saarland University (Germany) Guest Editors * Valia Kordoni, DFKI GmbH and Saarland University (Germany) * Carlos Ramisch, University of Grenoble (France) and Federal University of Rio Grande do Sul (Brazil) * Aline Villavicencio, Federal University of Rio Grande do Sul (Brazil) and Massachusetts Institute of Technology (USA) Contact For any inquiries regarding the special issue, please send an email to mweguesteditor@gmail.com
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| 7-6 | CfP Special issue of EURASIP Journal on Audio, Speech, and Music Processing: Sparse Modeling for Speech and Audio Processing Call for Papers EURASIP Journal on Audio, Speech, and Music Processing Special Issue on Sparse Modeling for Speech and Audio Processing Sparse modeling and compressive sensing are rapidly developing fields in a variety of signal processing and machine learning conferences, focused on the problems of variable selection in high-dimensional datasets and signal reconstruction from few training examples. With the increasing amount of high-dimensional speech and audio data available, the need to efficiently represent and search through these data spaces is becoming of vital importance. The challenges arise from selecting highly predictive signal features and adaptively finding a dictionary which best represents the signal. Overcoming these challenges is likely to require efficient and effective algorithms, mainly focused on l1-regularized optimization, basis pursuit, Lasso sparse regression, missing data problem and various extensions. Despite the significant advances in the fields, there are a number of open issues remain when realizing sparse model in real-life applications, e.g. stability and interpretability of sparse models, model selection, group/fused sparsity, and evaluation of the results. Furthermore, sparse modeling has ubiquitous applications in speech and audio processing areas, including dimensionality reduction, model regularization, speech/audio compression/reconstruction, acoustic/audio feature selection, acoustic modeling, speech recognition, blind source separation, and many others. Our goal aims to come up with a set of new algorithms/applications and to advance the state of the arts in speech and audio processing. In light of the sufficiently growing research activities and their importance, we openly invite papers describing various aspects of sparsity modeling and related techniques as well as their successful applications. Submissions must not have been previously published and must have specific connection to audio, speech, and music processing. The topics of particular interest will include, but are not limited to: • Sparse representation and compressive sensing • Sparse modeling and regression • Sparse modeling for model regularization • Sparse modeling for speech recognition • Sparse modeling for language processing • Sparse modeling for source separation • Sparse modeling for music processing • Deep learning for sparse models • Practical applications of sparse modeling • Machine learning algorithms, techniques and applications Before submission authors should carefully read over the journal’s Instructions for Authors, which are located at http://asmp.eurasipjournals.com/authors/instructions. Prospective authors should submit an electronic copy of their complete manuscript through the SpringerOpen submission system at http://asmp.eurasipjournals.com/manuscript, according to the following timetable: Manuscript Due: June 15, 2012 First Round of Reviews: September 1, 2012 Publication Date: December 1, 2012 Guest editors: Jen-Tzung Chien (E-mail: jtchien@mail.ncku.edu.tw) National Cheng Kung University, Tainan, Taiwan Bhuvana Ramabhadran (E-mail: bhuvana@us.ibm.com) IBM T. J. Watson Research Center, Yorktown Heights, NY, USA Tomoko Matsui (E-mail: tmatsui@ism.ac.jp) The Institute of Statistical Mathematics, Tokyo, Japan
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| 7-7 | CfP Phonetica: Speech Production and Perception across the Segment-Prosody Divide: Data – Theory – Modelling Call for Papers
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