V
Victor Abrash
Researcher at SRI International
Publications - 28
Citations - 971
Victor Abrash is an academic researcher from SRI International. The author has contributed to research in topics: Hidden Markov model & Multilayer perceptron. The author has an hindex of 15, co-authored 28 publications receiving 945 citations.
Papers
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SRILM at Sixteen: Update and Outlook
TL;DR: Developments in the SRI Language Modeling Toolkit since 2002 are reviewed, including measures to make training from large data sets more efficient, to implement additional language modeling techniques, and for client/server operation.
Proceedings Article
Connectionist speaker normalization and adaptation.
TL;DR: This paper explores supervised speaker adaptation and normalization in the MLP component of a hybrid hidden Markov model/ multilayer perceptron version of SRI's DECIPHERTM speech recognition system.
The SRI EduSpeak System: Recognition and Pronunciation Scoring for Language Learning
Horacio Franco,Victor Abrash,Kristin Precoda,Harry Bratt,Ramana Rao,John Butzberger,Romain Rossier,Federico Cesari +7 more
TL;DR: This work reports results on the application of adaptation techniques to recognize both native and nonnative speech in a speaker-independent manner and discusses the pronunciation scoring paradigm and shows experimental results in the form of correlations between the pronunciation quality estimators included in the toolkit and grades given by human listeners.
Journal ArticleDOI
EduSpeak[R]: A Speech Recognition and Pronunciation Scoring Toolkit for Computer-Aided Language Learning Applications
Horacio Franco,Harry Bratt,Romain Rossier,Venkata Ramana Rao Gadde,Elizabeth Shriberg,Victor Abrash,Kristin Precoda +6 more
TL;DR: This work reviews the approach to pronunciation scoring, where the aim is to estimate the grade that a human expert would assign to the pronunciation quality of a paragraph or a phrase and evaluates different machine scores that can be used as predictor variables to estimate pronunciation quality.
Patent
Method and apparatus for obtaining complete speech signals for speech recognition applications
TL;DR: In this paper, the authors present a method and apparatus for obtaining complete speech signals for speech recognition applications using a Hidden Markov Model (HMM) and a sequence of frames.