DocumentCode :
3425556
Title :
Irrelevant variability normalization based HMM training using map estimation of feature transforms for robust speech recognition
Author :
Zhu, Donglai ; Huo, Qiang
Author_Institution :
Inst. for Infocomm Res., Singapore
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
4717
Lastpage :
4720
Abstract :
In the past several years, we\´ve been studying feature transformation (FT) approaches to robust automatic speech recognition (ASR) which can compensate for possible "distortions" caused by factors irrelevant to phonetic classification in both training and recognition stages. Several FT functions with different degrees of flexibility have been studied and the corresponding maximum likelihood (ML) training techniques developed. In this paper, we study yet another new FT function which takes the most flexible form of frame-dependent linear transformation. Maximum a posteriori (MAP) estimation is used for estimating FT function parameters to deal with the possible problem of insufficient training data caused by the increased number of model parameters. The effectiveness of the proposed approach is confirmed by evaluation experiments on Finnish Aurora3 database.
Keywords :
hidden Markov models; maximum likelihood estimation; speech recognition; Finnish Aurora3 database; HMM training; MAP estimation; feature transforms; frame-dependent linear transformation; hidden Markov model; irrelevant variability normalization; maximum a posteriori; maximum likelihood training; phonetic classification; robust speech recognition; Asia; Automatic speech recognition; Electronic mail; Gaussian distribution; Hidden Markov models; Maximum likelihood estimation; Robustness; Speech recognition; Support vector machines; Training data; MAP estimate; feature transformation; hidden Markov model; robust speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4518710
Filename :
4518710
Link To Document :
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