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Fast and Low Power Viterbi Search Engine Using Inverse Hidden Markov Model
Bo-Sung KIM Jun-Dong CHO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2004/03/01
Print ISSN: 0916-8508
Type of Manuscript: Special Section LETTER (Special Section on Applications and Implementations of Digital Signal Processing)
Category: Communication Theory and Systems
VLSI, HMM, Viterbi search, low-power,
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Viterbi search engine in speech recognition consumes many computation time and hardware resource for finding maximum likelihood in HMM (Hidden Markov Model). We propose a fast Viterbi search engine using IHMM (Inverse Hidden Markov Model). A benefit of this method is that we can remove redundant computation of path matrix. The power consumption and the computational time are reduced by 68.6% at the 72.9% increase in terms of the number of gates.