DocumentCode :
3109914
Title :
A Hybrid Algorithm for Estimation of the Parameters of Hidden Markov Model based Acoustic Modeling of Speech Signals using Constraint-Based Genetic Algorithm and Expectation Maximization
Author :
Huda, Md Shamsul ; Yearwood, John ; Ghosh, Ranadhir
Author_Institution :
Univ. of Ballarat, Ballarat
fYear :
2007
fDate :
11-13 July 2007
Firstpage :
438
Lastpage :
443
Abstract :
The conventional method for estimation of the parameters of hidden Markov model (HMM) based acoustic modeling of speech signals uses the expectation-maximization (EM) algorithm. But the EM algorithm is highly sensitive to initial values of model parameters and does not guarantee convergence to a global maximum resulting in non-optimized estimation for the HMM and lower recognition accuracy. We propose a genetic algorithm (GA) based EM learning method (GA-EHMM) for estimation of the HMM parameters. GA explores the search space more thoroughly than that of the EM algorithm and enables the EM to escape from many local maxima. A constraint-based approach of GA has been adopted in "GA-EHMM" which directs GA towards promising regions of the search space. Instead of generating the initial GA population randomly, a variable segmentation technique is used in the HMM initialization process. "GA-EHMM" has been tested on the TIMIT speech corpus. Experimental results show that "GA- EHMM" obtains better values for the likelihood function as well as higher recognition accuracy than that of the HMM model trained by the standard EM algorithm.
Keywords :
acoustic signal processing; expectation-maximisation algorithm; genetic algorithms; hidden Markov models; learning (artificial intelligence); speech recognition; EM learning method; constraint-based genetic algorithm; expectation maximization; hidden Markov model based acoustic modeling; parameter estimation; speech recognition; variable segmentation technique; Australia; Automatic speech recognition; Constraint optimization; Convergence; Genetic algorithms; Hidden Markov models; Informatics; Iterative algorithms; Parameter estimation; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS International Conference on
Conference_Location :
Melbourne, Qld.
Print_ISBN :
0-7695-2841-4
Type :
conf
DOI :
10.1109/ICIS.2007.23
Filename :
4276421
Link To Document :
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