An algorithm for optimizing the nonlinear time alignment based on tabu approach

Mei Xiaodan; Suu Shenghe

Volume 24 Issue 1

Jan. 2002

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Mei Xiaodan, Suu Shenghe. An algorithm for optimizing the nonlinear time alignment based on tabu approach[J]. Journal of Electronics & Information Technology, 2002, 24(1): 31-36.

Citation:

Mei Xiaodan, Suu Shenghe. An algorithm for optimizing the nonlinear time alignment based on tabu approach[J]. Journal of Electronics & Information Technology, 2002, 24(1): 31-36.

Mei Xiaodan, Suu Shenghe. An algorithm for optimizing the nonlinear time alignment based on tabu approach[J]. Journal of Electronics & Information Technology, 2002, 24(1): 31-36.

Citation:

Mei Xiaodan, Suu Shenghe. An algorithm for optimizing the nonlinear time alignment based on tabu approach[J]. Journal of Electronics & Information Technology, 2002, 24(1): 31-36.

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An algorithm for optimizing the nonlinear time alignment based on tabu approach

Received Date: 2000-03-06
Rev Recd Date: 2000-07-28
Publish Date: 2002-01-19

Abstract

Abstract

Dynamic Time Warping(DTW) has been widely used in speech recognition systems as a nonlinear time alignment technique. It uses the dynamic programming technique to search the optimal warping path for two time sequences. Although this algorithm needs less computation and shorter training and searching time, it is a local optimization algorithm. The Tabu Search (TS) algorithm is the generalized heuristic global search technique with short-time memory, and suitable for solving many nonlinear optimization problems. This paper applies this technique to speech recognition systems, and presents a new algorithm for optimizing time warping based on TS approach, which makes time warping functions optimized globally. Simulation results show that TSTW has better time warping performance than DTW and GTW.

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References(1)

References

F. Itakura, Minimum prediction residual principle applied to speech recognition, IEEE Trans. on ASSP, 1975, ASSP-23(2), 67-72.[2]S.K. Wong, C. W. Chau, W. A. Halang, Genetic algorithm for optimizing the nonlinear time alignment of automatic speech recognition system, IEEE Trans. on Industrial Electronic, 1996,IE-43(5), 559-566.[3]H.F. Silverman, D. P. Morgan, The application of dynamic programming to connected speech recognition, IEEE ASSP Magazine, 1990, 38(7), 7-24.[4]L.R. Rabiner, S. E. Levinson, Isolated and connected word recognition-theory and selected applications, IEEE Trans. on Communications, 1981, COM-29(5), 621-658.[5]F. Glover, M. Laguna, Tabu Search, Kluwer Academic Publishers, London, 1997, I chapter. ［6］H. Sakoe, S. Chiba, Dynamic programming algorithm optimization for spoken word recognition,IEEE Trans. on ASSP, 1978, ASSP-26(2), 43-49.

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