Optimization of HMM by the tabu search algorithm

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 mecha-nism 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