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Recognition of Isolated Digits Using Fuzzy Matrix Quantization
Satoshi KONDO Akio OGIHARA Shojiro YONEDA
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1991/10/25
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Issue on JTC-CSCC '90)
Category: Speech and Image Processing
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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.