Keywords: tabu search, hidden Markov model, forward-backward algorithm, speech recognition, global optimum
Received March 12, 2001; revised April 25, 2003; accepted July 14, 2003.
Communicated by C. C. Jay Kuo.
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Tsong-Yi Chen, Xiao-Dan Mei*, Jeng-Shyang Pan
and Sheng-He Sun*
Department of Electronic Engineering
National Kaohsiung University of Applied Sciences
Kaohsiung, 807 Taiwan
*Department of Automatic Test, Measurement and Control
Harbin Institute of Technology
Harbin, 150001 China
In this paper, a simple version of the tabu search algorithm is employed to train a Hidden Markov Model (HMM) to search out the optimal parameter structure of HMM for automatic speech recognition. The proposed TS-HMM training provides a mechanism that allows the search process to escape from a local optimum and obtain a near global optimum. Experimental results show that the TS-HMM training has a higher probability of finding the optimal model parameters than traditional algorithms do.
Received March 12, 2001; revised April 25, 2003; accepted July 14, 2003.
Communicated by C. C. Jay Kuo.