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
         
        
        
        
        
        
            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;
         
        
        
        
            Conference_Titel : 
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
         
        
            Conference_Location : 
Taipei
         
        
        
            Print_ISBN : 
978-1-4244-2353-8
         
        
            Electronic_ISBN : 
1520-6149
         
        
        
            DOI : 
10.1109/ICASSP.2009.4960447