Fast and Low Power Viterbi Search Engine Using Inverse Hidden Markov Model
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.3 pp.695-697
- Publication Date
- 2004/03/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section LETTER (Special Section on Applications and Implementations of Digital Signal Processing)
- Category
- Communication Theory and Systems
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Bo-Sung KIM, Jun-Dong CHO, "Fast and Low Power Viterbi Search Engine Using Inverse Hidden Markov Model" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 3, pp. 695-697, March 2004, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_3_695/_p
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@ARTICLE{e87-a_3_695,
author={Bo-Sung KIM, Jun-Dong CHO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fast and Low Power Viterbi Search Engine Using Inverse Hidden Markov Model},
year={2004},
volume={E87-A},
number={3},
pages={695-697},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={March},}
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TY - JOUR
TI - Fast and Low Power Viterbi Search Engine Using Inverse Hidden Markov Model
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 695
EP - 697
AU - Bo-Sung KIM
AU - Jun-Dong CHO
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2004
AB - 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.
ER -
English
