NDLI: A Markov random field approach to Bayesian speaker adaptation

Content Provider	IEEE Xplore Digital Library
Author	Shahshahani, B.M.
Copyright Year	1993
Abstract	Speaker adaptation through Bayesian learning methodology is studied in this paper. In order to utilize the cross allophone correlations, a Markov random field (MRF) model is proposed as the joint prior distribution of the mean vectors of the allophones. Neighborhoods are defined as pairs of parameters between which strong correlations have been observed previously. Maximum a posteriori estimates of the mean vectors are obtained through an iterative optimization technique that converges to the global maximum of the posterior distribution. This process is similar to a recursive prediction of the parameters, where at each iteration each parameter is estimated by a weighted sum of two terms, the first predicted by the neighbors and the second by the samples. Further Bayesian smoothing of the output distributions is carried out by utilizing some simplifications on the functional forms of the marginal posterior distributions. The proposed method is fast, consuming only a few CPU minutes for processing hundreds of sentences from a new speaker on an IBM RS6000 Model 580 system. Experimental results show rapid improvement of recognition accuracy.
Starting Page	183
Ending Page	191
Page Count	9
File Size	233483
File Format	PDF
ISSN	10636676
Volume Number	5
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-03-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov random fields Bayesian methods Maximum a posteriori estimation Parameter estimation Recursive estimation Smoothing methods Vocabulary Speech recognition Data mining Training data
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Markov random field approach to Bayesian speaker adaptation

Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains

A maximum a posteriori approach to speaker adaptation using the trended hidden Markov model

Speaker adaptation using an eigenphone basis

Speaker adaptation using combined transformation and Bayesian methods

Speaker adaptation using constrained estimation of Gaussian mixtures

Speaker adaptation based on transfer vector field smoothing using maximum a posteriori probability estimation

Speaker adaptation using constrained transformation

Variational bayesian estimation and clustering for speech recognition

A Markov random field approach to Bayesian speaker adaptation

Similar Documents

A Markov random field approach to Bayesian speaker adaptation

Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains

A maximum a posteriori approach to speaker adaptation using the trended hidden Markov model

Speaker adaptation using an eigenphone basis

Speaker adaptation using combined transformation and Bayesian methods

Speaker adaptation using constrained estimation of Gaussian mixtures

Speaker adaptation based on transfer vector field smoothing using maximum a posteriori probability estimation

Speaker adaptation using constrained transformation

Variational bayesian estimation and clustering for speech recognition

A Markov random field approach to Bayesian speaker adaptation