NDLI: Bayesian latent variable models for speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Jen-Tzung Chien Peng Liu
Copyright Year	2013
Description	Author affiliation: Sohu.com Inc., Beijing, China (Peng Liu) \|\| Dept. of Electr. & Comput. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan (Jen-Tzung Chien)
Abstract	We present a Bayesian framework to learn prior and posterior distributions for latent variable models. Our goal is to deal with model regularization and achieve desirable prediction using heterogeneous speech data. A variational Bayesian expectation-maximization algorithm is developed to establish a latent variable model based on the exponential family distributions. This algorithm does not only estimate model parameters but also their hyperparameters which reflect the model uncertainties. The uncertainty is compensated to construct a variety of regularized models. We realize this full Bayesian framework for uncertainty decoding of speech signals. Compared to maximum likelihood method and Bayesian approach with heuristically-selected hyperparameters, the proposed method achieves higher speech recognition accuracy especially in case of sparse and noisy training data.
Starting Page	7393
Ending Page	7397
File Size	262972
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6639099
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayes methods Hidden Markov models Speech recognition Training Training data Computational modeling Speech Speech Recognition Bayesian Learning Exponential Family Latent Variable Model
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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