SPOKEN WORD RECOGNITION USING THE ARTIFICIAL EVOLUTION OF A SET OF VOCABULARY

TomioTakara  EijiNagaki  

【摘要】：正Hidden Markov models (HMMs) are widely used for automatic speech recognition. However, there is a problem still unresolved, i.e. how to design the optimal structure of the HMM. As an answer to this problem, we proposed the application of a genetic algorithm (GA) to search out such an optimal structure, and we showed this method to be effective for isolated word recognition. In these applications, the evolutions occurred at each word class independently. However, many isolated word recognition systems are performed using a set of vocabulary. Therefore, the artificial evolution using the vocabulary set is thought to be more effective. In this paper, we propose the spoken word recognition using the artificial evolution of a set of vocabulary.

【作者单位】：Department of Information Engineering, University of the Ryukyus 1 Senbaru, Nishihara Okinawa 903-0213 JAPAN Department of Information Engineering, University of the Ryukyus 1 Senbaru, Nishihara Okinawa 903-0213 JAPAN
【分类号】：H08
【正文快照】：

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