DocumentCode :
3527000
Title :
Generalized Baum-Welch algorithm for discriminative training on large vocabulary continuous speech recognition system
Author :
Hsiao, Roger ; Tam, Yik-Cheung ; Schultz, Tanja
Author_Institution :
Language Technol. Inst., Carnegie Mellon Univ., Pittsburgh, PA
fYear :
2009
fDate :
19-24 April 2009
Firstpage :
3769
Lastpage :
3772
Abstract :
We propose a new optimization algorithm called Generalized Baum Welch (GBW) algorithm for discriminative training on hidden Markov model (HMM). GBW is based on Lagrange relaxation on a transformed optimization problem. We show that both Baum-Welch (BW) algorithm for ML estimate of HMM parameters, and the popular extended Baum-Welch (EBW) algorithm for discriminative training are special cases of GBW.We compare the performance of GBW and EBW for Farsi large vocabulary continuous speech recognition (LVCSR).
Keywords :
hidden Markov models; maximum likelihood estimation; optimisation; speech recognition; vocabulary; Lagrange relaxation algorithm; ML estimation; discriminative training; generalized Baum-Welch algorithm; hidden Markov model; large vocabulary continuous speech recognition system; maximum likelihood estimation; optimization algorithm; Hidden Markov models; Lagrangian functions; Large-scale systems; Lattices; Maximum likelihood estimation; Mutual information; Natural languages; Parameter estimation; Speech recognition; Vocabulary; Speech recognition; discriminative training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
ISSN :
1520-6149
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2009.4960447
Filename :
4960447
Link To Document :
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