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

Content Provider	IEEE Xplore Digital Library
Author	Mitchell, C.D. Helzerman, R.A. Jamieson, L.H. Harper, M.P.
Copyright Year	1993
Description	Author affiliation: Sch. of Electr. Eng., Purdue Univ., W. Lafayette, IN, USA (Mitchell, C.D.; Helzerman, R.A.; Jamieson, L.H.; Harper, M.P.)
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 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.
Starting Page	298
Ending Page	306
File Size	754256
Page Count	9
File Format	PDF
ISBN	081864222X
DOI	10.1109/SPDP.1993.395519
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-12-01
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Speech recognition Costs Parallel processing Computational efficiency Natural languages Data structures System testing Automatic speech recognition Neodymium
Content Type	Text
Resource Type	Article

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