A parallel implementation of a hidden Markov model with duration modeling for speech recognition

This paper describes a parallel implementation of a Hidden Markov Model (HMM) for spoken language recognition on the MasPar MP-1. By exploiting the massive parallelism of explicit duration HMMs, we can develop more complex models for real-time speech recognition. Implementational issues such as choice of data structures, method of communication, and utilization of parallel functions are explored. The results of our experiments show that the parallelism in HMMs can be effectively exploited by the MP-1. Training that use to take nearly a week can now be completed in about an hour. The system can recognize the phones of a test utterance in a fraction of a second.<
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