IEICE TRANSACTIONS on Fundamentals
Recognition of Isolated Digits Using Fuzzy Matrix Quantization
Summary :
This study proposes fuzzy matrix quantization (FMQ) which is a new coding technique developed to obtain discrete symbols employed for hidden Markov models (HMM's). FMQ is a coding technique combining fuzzy vector quantization with matrix quantization. The validity of FMQ is evaluated by a speaker-independent isolated word recognition task. First, the effect of FMQ is examined when FMQ is applied to the training phase and/or recognition phase. The effects of number of training data, codebook size and codeword matrix size for recognition accuracy are investigated. And the results of the speech recognition based on HMM recognizer using FMQ technique is compared with HMM recognizers using conventional quantization methods, vector quantization and fuzzy vector quantization. As a result, FMQ is the effective coding technique for isolated word recognition on condition that codebook size is large, above all, when FMQ is applied to the training phase and training data set is small.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E74-A No.10 pp.3034-3040
- Publication Date
- 1991/10/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on JTC-CSCC '90)
- Category
- Speech and Image Processing
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Keyword
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Satoshi KONDO, Akio OGIHARA, Shojiro YONEDA, "Recognition of Isolated Digits Using Fuzzy Matrix Quantization" in IEICE TRANSACTIONS on Fundamentals,
vol. E74-A, no. 10, pp. 3034-3040, October 1991, doi: .
Abstract: This study proposes fuzzy matrix quantization (FMQ) which is a new coding technique developed to obtain discrete symbols employed for hidden Markov models (HMM's). FMQ is a coding technique combining fuzzy vector quantization with matrix quantization. The validity of FMQ is evaluated by a speaker-independent isolated word recognition task. First, the effect of FMQ is examined when FMQ is applied to the training phase and/or recognition phase. The effects of number of training data, codebook size and codeword matrix size for recognition accuracy are investigated. And the results of the speech recognition based on HMM recognizer using FMQ technique is compared with HMM recognizers using conventional quantization methods, vector quantization and fuzzy vector quantization. As a result, FMQ is the effective coding technique for isolated word recognition on condition that codebook size is large, above all, when FMQ is applied to the training phase and training data set is small.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e74-a_10_3034/_p
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@ARTICLE{e74-a_10_3034,
author={Satoshi KONDO, Akio OGIHARA, Shojiro YONEDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Recognition of Isolated Digits Using Fuzzy Matrix Quantization},
year={1991},
volume={E74-A},
number={10},
pages={3034-3040},
abstract={This study proposes fuzzy matrix quantization (FMQ) which is a new coding technique developed to obtain discrete symbols employed for hidden Markov models (HMM's). FMQ is a coding technique combining fuzzy vector quantization with matrix quantization. The validity of FMQ is evaluated by a speaker-independent isolated word recognition task. First, the effect of FMQ is examined when FMQ is applied to the training phase and/or recognition phase. The effects of number of training data, codebook size and codeword matrix size for recognition accuracy are investigated. And the results of the speech recognition based on HMM recognizer using FMQ technique is compared with HMM recognizers using conventional quantization methods, vector quantization and fuzzy vector quantization. As a result, FMQ is the effective coding technique for isolated word recognition on condition that codebook size is large, above all, when FMQ is applied to the training phase and training data set is small.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - Recognition of Isolated Digits Using Fuzzy Matrix Quantization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3034
EP - 3040
AU - Satoshi KONDO
AU - Akio OGIHARA
AU - Shojiro YONEDA
PY - 1991
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E74-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 1991
AB - This study proposes fuzzy matrix quantization (FMQ) which is a new coding technique developed to obtain discrete symbols employed for hidden Markov models (HMM's). FMQ is a coding technique combining fuzzy vector quantization with matrix quantization. The validity of FMQ is evaluated by a speaker-independent isolated word recognition task. First, the effect of FMQ is examined when FMQ is applied to the training phase and/or recognition phase. The effects of number of training data, codebook size and codeword matrix size for recognition accuracy are investigated. And the results of the speech recognition based on HMM recognizer using FMQ technique is compared with HMM recognizers using conventional quantization methods, vector quantization and fuzzy vector quantization. As a result, FMQ is the effective coding technique for isolated word recognition on condition that codebook size is large, above all, when FMQ is applied to the training phase and training data set is small.
ER -
English
