NDLI: Gaussian Mixture Optimization Based on Efficient Cross-Validation

Content Provider	IEEE Xplore Digital Library
Author	Shinozaki, T. Furui, S. Kawahara, T.
Copyright Year	2007
Abstract	A Gaussian mixture optimization method is developed by using the cross-validation (CV) likelihood as an objective function instead of the conventional training set likelihood. The optimization is based on reducing the number of mixture components by selecting and merging pairs of Gaussians step by step according to the objective function so as to remove redundant components and improve the generality of the model. The CV likelihood is more effective for avoiding over-fitting than is the conventional likelihood, and it provides a termination criterion that does not rely on empirical thresholds. While the idea is simple, one problem is its infeasible computational cost. To make such optimization practical, an efficient evaluation algorithm using sufficient statistics is proposed. In addition, aggregated CV (AgCV) is developed to further improve the generalization performance of CV. Large-vocabulary speech recognition experiments on oral presentations show that the proposed methods improve speech recognition performance with automatically determined model complexity. The AgCV-based optimization is computationally more expensive than the CV-based method but gives better recognition performance.
Sponsorship	IEEE Signal Processing Society
Starting Page	540
Ending Page	547
Page Count	8
File Size	678107
File Format	PDF
ISSN	19324553
Volume Number	4
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Speech recognition Parameter estimation Optimization methods Merging Training data Computational efficiency Statistical distributions Gaussian distribution Signal processing algorithms sufficient statistics Cross-validation Gaussian mixture hidden Markov model (HMM) speech recognition
Content Type	Text
Resource Type	Article
Subject	Signal Processing 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

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