DocumentCode
768231
Title
Improved generalization of MCE parameter estimation with application to speech recognition
Author
Purnell, Darryl William ; Botha, Elizabeth C.
Author_Institution
Dept. of Electr., Electron. & Comput. Eng., Pretoria Univ., South Africa
Volume
10
Issue
4
fYear
2002
fDate
5/1/2002 12:00:00 AM
Firstpage
232
Lastpage
239
Abstract
Discriminative training of hidden Markov models (HMMs) using minimum classification error training (MCE) has been shown to work well for certain speech recognition applications. MCE is, however, somewhat prone to overspecialization. This study investigates various techniques which improve performance and generalization of the MCE algorithm. Improvements of up to 10% in relative error rate on the test set are achieved for the TIMIT dataset
Keywords
error analysis; hidden Markov models; parameter estimation; signal classification; speech recognition; MCE algorithm generalization; MCE parameter estimation; TIMIT dataset; discriminative training; error rate; hidden Markov models; minimum classification error; speech recognition; Acoustic testing; Automatic speech recognition; Error analysis; Error correction; Hidden Markov models; Multi-layer neural network; Neural networks; Parameter estimation; Speech recognition; Viterbi algorithm;
fLanguage
English
Journal_Title
Speech and Audio Processing, IEEE Transactions on
Publisher
ieee
ISSN
1063-6676
Type
jour
DOI
10.1109/TSA.2002.1011536
Filename
1011536
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