NDLI: GMM and HMM training by aggregated EM algorithm with increased ensemble sizes for robust parameter estimation

Content Provider	IEEE Xplore Digital Library
Author	Shinozaki, T. Kawahara, T.
Copyright Year	2008
Description	Author affiliation: Tokyo Inst. of Technol., Tokyo (Shinozaki, T.; Kawahara, T.)
Abstract	In order to compensate for the weaknesses of the expectation maximization (EM) algorithm to over-training and to improve model performance for new data, we have recently proposed aggregated EM (Ag-EM) algorithm that introduces bagging like approach in the framework of the EM algorithm and have shown that it gives similar improvements as cross-validation EM (CV-EM) over conventional EM. However, a limitation with the experiments was that the number of multiple models used in the aggregation operation or the ensemble size was fixed to a small value. Here, we investigate the relationship between the ensemble size and the performance as well as giving a theoretical discussion with the order of the computational cost. The algorithm is first analyzed using simulated data and then applied to large vocabulary speech recognition on oral presentations. Both of these experiments show that Ag-EM outperforms CV-EM by using larger ensemble sizes.
Starting Page	4405
Ending Page	4408
File Size	182731
Page Count	4
File Format	PDF
ISBN	9781424414833
ISSN	15206149
DOI	10.1109/ICASSP.2008.4518632
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Robustness Parameter estimation Bagging Computational efficiency Data analysis Speech analysis Algorithm design and analysis Computational modeling Analytical models hidden Markov model Expectation maximization algorithm ensemble training bagging sufficient statistics
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

Gmm and hmm training by aggregated em algorithm with increased ensemble sizes for robust parameter estimation.

Modeling speech parameter sequences with latent trajectory Hidden Markov model

Robust Diagnosis of Operating Mode Based on Time-Varying Hidden Markov Models

Factor analyzed voice models for HMM-based speech synthesis

Noisy hidden Markov models for speech recognition

Incremental ML estimation of HMM parameters for efficient training

HMM parameters estimation using hybrid Baum-Welch genetic algorithm

HMM training based on CV-EM and CV Gaussian mixture optimization

Optimal Operators of Hybrid Genetic Algorithm for GMM Parameter Estimation

GMM and HMM training by aggregated EM algorithm with increased ensemble sizes for robust parameter estimation

Similar Documents

Gmm and hmm training by aggregated em algorithm with increased ensemble sizes for robust parameter estimation.

Modeling speech parameter sequences with latent trajectory Hidden Markov model

Robust Diagnosis of Operating Mode Based on Time-Varying Hidden Markov Models

Factor analyzed voice models for HMM-based speech synthesis

Noisy hidden Markov models for speech recognition

Incremental ML estimation of HMM parameters for efficient training

HMM parameters estimation using hybrid Baum-Welch genetic algorithm

HMM training based on CV-EM and CV Gaussian mixture optimization

Optimal Operators of Hybrid Genetic Algorithm for GMM Parameter Estimation

GMM and HMM training by aggregated EM algorithm with increased ensemble sizes for robust parameter estimation