A Parallel Implementation of a Hidden Markov
نویسندگان
چکیده
with Duration Modeling for Speech Recognition y Carl D. Mitchell, Randall A. Helzerman, Leah H. Jamieson, and Mary P. Harper School of Electrical Engineering, Purdue University West Lafayette, IN 47907-1285 fcdm,helz,lhj,[email protected] Abstract 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 e ectively 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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