Presentation 2003/10/24
The application of HMM and cepstral coefficient in EMG-based unvoiced speech recognition
Zhipeng ZHANG, Hiroyuki MANABE, Tsutomu HORIKOSHI, Toshiaki SUGIMURA,
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Abstract(in English) A new technique for unvioced speech recognition via EMG signal is presented. Two methods (HMM for EMG-based speech recognition and cepstral coefficient of EMG as feature vector) are described. The HMM which is a useful statistical tool for time serial signal is applied to EMG signal. The LPC analysis is applied for EMG signal and the cepstral coefficient is used as the feature for EMG-based speech recognition. The EMG signals of 10 repetitions of 14 isolated Japanese digits were analyzed. Experimental result showed that HMM was effective in dealing with EMG time serial signal. Another experimental result showed that the mean recognition accuracy of the cepstral coefficient was 9.3% superior to that of using power of EMG signal only as feature.
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Keyword(in English) unvoiced speech recognition / EMG / cepstral / HMM / LPC
Paper # SP2003-104,WIT2003-16
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Committee WIT
Conference Date 2003/10/24(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) The application of HMM and cepstral coefficient in EMG-based unvoiced speech recognition
Sub Title (in English)
Keyword(1) unvoiced speech recognition
Keyword(2) EMG
Keyword(3) cepstral
Keyword(4) HMM
Keyword(5) LPC
1st Author's Name Zhipeng ZHANG
1st Author's Affiliation Multimedia Laboratories, NTT DoCoMo()
2nd Author's Name Hiroyuki MANABE
2nd Author's Affiliation Multimedia Laboratories, NTT DoCoMo
3rd Author's Name Tsutomu HORIKOSHI
3rd Author's Affiliation Multimedia Laboratories, NTT DoCoMo
4th Author's Name Toshiaki SUGIMURA
4th Author's Affiliation Multimedia Laboratories, NTT DoCoMo
Date 2003/10/24
Paper # SP2003-104,WIT2003-16
Volume (vol) vol.103
Number (no) 402
Page pp.pp.-
#Pages 6
Date of Issue