Genetic algorithm for optimizing the nonlinear time alignment of automatic speech recognition systems

Dynamic time warping (DTW) is a nonlinear time-alignment technique for automatic speech recognition (ASR) systems. It had been widely used in many commercial and industrial products, ranging from electronic dailies/dictionaries to wireless voice digit dialers. DTW has the advantages of fast training and searching times, which makes it more popular than other available ASR techniques. However, there exist some limitations to DTW, such as the stringent rule on slope weighting, the nontrivial computation of the K-best paths, and the significant increase in computational time when the endpoint constraint is relaxed or the variations of the length of pattern increased. In this paper, a stochastic method called the genetic algorithm (GA), which is used to solve the nonlinear time alignment problem, is presented. Experimental results show that the GA has a better performance than the DTW. In addition, two derivatives of GA: the hybrid GA and the parallel GA are also presented