A speech recognizer with selectable model parameters

This paper presents the design and simulation results of a hidden Markov model (HMM) based isolated word recognizer IC. The new design can handle any combination of states and mixtures (up to 16 states and 8 mixtures). The speech IC has been verified with 353 test speech data. The recognition accura...

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Bibliographic Details
Main Authors: Wei Han, Cheong-Fat Chan, Chiu-Sing Choy, Kong-Pang Pun
Format: Conference Proceeding
Language:English
Subjects:
Character recognition
Design engineering
Hidden Markov models
Integrated circuit modeling
Integrated circuit testing
Pattern recognition
Probability density function
Probability distribution
Speech recognition
Viterbi algorithm
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Description
Summary:This paper presents the design and simulation results of a hidden Markov model (HMM) based isolated word recognizer IC. The new design can handle any combination of states and mixtures (up to 16 states and 8 mixtures). The speech IC has been verified with 353 test speech data. The recognition accuracy is 93.8% (48-bit) with no truncation and 88.9% with truncation (16-bit).
ISSN:0271-4302
2158-1525
DOI:10.1109/ISCAS.2005.1465967