부산대학교 도서관

Hidden Markov models (HMMs) are widely used for automatic speech recognition because they have a powerful algorithm used in estimating the models parameters, and achieve a high performance. Once a structure of the model is given, the model's parameters are obtained automatically by feeding training data. There is, however, no effective design method leading to an optimal structure of the HMMs. We propose a new application of a genetic algorithm to search out such an optimal structure. In this method, the left-right structures are adopted for HMMs and the likelihood is used for the fitness of the genetic algorithm. We report the results of our experiment showing the effectiveness of the genetic algorithm in automatic speech recognition.