NDLI: Modeling long term variability information in a mixture stochastic trajectory framework

Content Provider	IEEE Xplore Digital Library
Author	Yifan Gong Illina, I. Haton, J.-P.
Copyright Year	1996
Description	Author affiliation: Inst. Nat. de Recherche en Inf. et Autom., Vandoeuvre-les-Nancy, France (Yifan Gong)
Abstract	The problem of acoustic modeling for speech recognizers is addressed. We distinguish two types of speech variability, long term (speaker identity, stationary noise, channel distortion) and short term (phoneme class). Currently, most recognizers model the two variabilities without considering their specificities, which may result in flat distributions with limited discriminability. In our system, the long term variability (environment) is modeled by a mixture model, where each mixture is modeled by a mixture stochastic trajectory model (MSTM). We propose the environment dependent mixture stochastic trajectory model (ED-MSTM) to model a set of environments. The parameters of ED-MSTM are estimated using the maximum likelihood (ML) estimation criterion by the expectation-maximisation (EM) algorithm. Our model has been tested on a 1011 word vocabulary, multi-speaker continuous French recognition task with noisy speech. In the experiments, we assume that speakers can be grouped into a pre-determined number of classes and that the class label of a speaker is missing. The use of environmental modeling cut down the error rate produced by the multi-speaker system by about 15%, which is a statistically significant improvement. The idea of environment modeling is applicable to other acoustic modeling techniques such as hidden Markov models.
Starting Page	334
Ending Page	337
File Size	364942
Page Count	4
File Format	PDF
ISBN	0780335554
DOI	10.1109/ICSLP.1996.607121
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-10-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Loudspeakers Maximum likelihood estimation Acoustic distortion Stochastic resonance Working environment noise Acoustic noise Stochastic processes Hidden Markov models Speech recognition Speech enhancement
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
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

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