Speech and Language processing as assistive technologies

We are delighted to bring you this special issue on speech and language processing for assistive technology. It addresses an important research area that is gaining increased recognition from researchers in speech and language processing as a rich and fulfilling area on which to focus their work, and by researchers in assistive technology as the means to dramatically improve communication technologies for individuals with disabilities. This special issue brings a wide swath of approaches and applications highlighting the variety this area offers.

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[10]  Steve Renals,et al.  Longitudinal study of ASR performance on ageing voices , 2008, INTERSPEECH.

[11]  Heidi Christensen,et al.  A comparative study of adaptive, automatic recognition of disordered speech , 2012, INTERSPEECH.

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[14]  Kathleen C. Fraser,et al.  Automated classification of primary progressive aphasia subtypes from narrative speech transcripts , 2014, Cortex.

[15]  Phil D. Green,et al.  Small-vocabulary speech recognition using a silent speech interface based on magnetic sensing , 2013, Speech Commun..

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[18]  Jun Cai,et al.  Recognition and Real Time Performances of a Lightweight Ultrasound Based Silent Speech Interface Employing a Language Model , 2011, INTERSPEECH.

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[20]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

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[29]  Gökhan Tür,et al.  Speech-based automated cognitive status assessment , 2010, INTERSPEECH.

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[31]  Frank Rudzicz,et al.  Acoustic transformations to improve the intelligibility of dysarthric speech , 2011 .

[32]  Hugo Van hamme,et al.  Accent recognition using i-vector, Gaussian Mean Supervector and Gaussian posterior probability supervector for spontaneous telephone speech , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

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[37]  Bhuvana Ramabhadran,et al.  Exemplar-based processing for speech recognition , 2012 .

[38]  Marc Brys,et al.  Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English , 2009 .

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[41]  Luís C. Oliveira,et al.  Jitter Estimation Algorithms for Detection of Pathological Voices , 2009, EURASIP J. Adv. Signal Process..

[42]  Steve J. Young,et al.  Error simulation for training statistical dialogue systems , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).

[43]  H. H. Clark,et al.  Using uh and um in spontaneous speaking , 2002, Cognition.

[44]  James Carmichael,et al.  A speech-controlled environmental control system for people with severe dysarthria. , 2007, Medical engineering & physics.

[45]  Raymond D. Kent,et al.  Toward an acoustic typology of motor speech disorders , 2003, Clinical linguistics & phonetics.

[46]  Hugo Van hamme,et al.  Weakly supervised keyword learning using sparse representations of speech , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[47]  Maysam Ghovanloo,et al.  A Wireless Magnetoresistive Sensing System for an Intraoral Tongue-Computer Interface , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[48]  Roy W Jones,et al.  Be concrete to be comprehended: Consistent imageability effects in semantic dementia for nouns, verbs, synonyms and associates , 2013, Cortex.

[49]  Jun Wang,et al.  Quantifying Articulatory Distinctiveness of Vowels , 2011, INTERSPEECH.

[50]  Norihiro Hagita,et al.  Automatic recognition of speech without any audio information , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[51]  Max A. Little,et al.  Nonlinear, Biophysically-Informed Speech Pathology Detection , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.