NDLI: Bayesian Sensing Hidden Markov Models

Content Provider	IEEE Xplore Digital Library
Author	Saon, G. Jen-Tzung Chien
Copyright Year	2006
Abstract	In this paper, we introduce Bayesian sensing hidden Markov models (BS-HMMs) to represent sequential data based on a set of state-dependent basis vectors. The goal of this work is to perform Bayesian sensing and model regularization for heterogeneous training data. By incorporating a prior density on sensing weights, the relevance of different bases to a feature vector is determined by the corresponding precision parameters. The BS-HMM parameters, consisting of the basis vectors, the precision matrices of sensing weights and the precision matrices of reconstruction errors, are jointly estimated by maximizing the likelihood function, which is marginalized over the weight priors. We derive recursive solutions for the three parameters, which are expressed via maximum a posteriori estimates of the sensing weights. We specifically optimize BS-HMMs for large-vocabulary continuous speech recognition (LVCSR) by introducing a mixture model of BS-HMMs and by adapting the basis vectors to different speakers. Discriminative training of BS-HMMs in the model domain and the feature domain is also proposed. Experimental results on an LVCSR task show consistent improvements due to the three sets of BS-HMM parameters and demonstrate how the extensions of mixture models, speaker adaptation, and discriminative training achieve better recognition results compared to those of conventional HMMs based on Gaussian mixture models.
Sponsorship	IEEE Signal Processing Society
Starting Page	43
Ending Page	54
Page Count	12
File Size	445672
File Format	PDF
ISSN	15587916
Volume Number	20
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Bayesian methods Sensors Covariance matrix Adaptation model Training Markov processes speech recognition acoustic model Bayesian learning dictionary learning discriminative training speaker adaptation
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering

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

Rapid discriminative acoustic model based on eigenspace mapping for fast speaker adaptation

Bayesian sensing hidden Markov models for speech recognition

Correctness-Adjusted Unsupervised Discriminative Acoustic Model Adaptation

Discriminative training for Bayesian sensing hidden Markov models

Adaptation of Hidden Markov Models Using Model-as-Matrix Representation

Sparse Inverse Covariance Matrices for Low Resource Speech Recognition

Some properties of Bayesian sensing hidden Markov models

Speaker-ensemble hidden Markov modeling for automatic speech recognition

Discriminatively Trained Sparse Inverse Covariance Matrices for Speech Recognition

Bayesian Sensing Hidden Markov Models

Similar Documents

Rapid discriminative acoustic model based on eigenspace mapping for fast speaker adaptation

Bayesian sensing hidden Markov models for speech recognition

Correctness-Adjusted Unsupervised Discriminative Acoustic Model Adaptation

Discriminative training for Bayesian sensing hidden Markov models

Adaptation of Hidden Markov Models Using Model-as-Matrix Representation

Sparse Inverse Covariance Matrices for Low Resource Speech Recognition

Some properties of Bayesian sensing hidden Markov models

Speaker-ensemble hidden Markov modeling for automatic speech recognition

Discriminatively Trained Sparse Inverse Covariance Matrices for Speech Recognition

Bayesian Sensing Hidden Markov Models