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.