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Journal of Computer Science & Systems Biology
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Research Article

Decoding Silent Speech in Japanese from Single Trial EEGS: Preliminary Results

Yamaguchi H1, Yamazaki T2*, Yamamoto K3, Ueno S4, Yamaguchi A5, Ito T2, Hirose S2, Kamijo K6, Takayanagi H7, Yamanoi T8 and Fukuzumi S1

1Knowledge Discovery Research Laboratories, NEC Corporation, Shimonumabe, Nakahara-ku, Kawasaki, Kanagawa, Japan

2Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Kawazu, Iizuka, Fukuoka, Japan

3Japan Tobacco Inc., Tokyo, Japan

4SCSK Corporation, Osaka, Japan

5Hitachi Systems Ltd., Tokyo, Japan

6Medical Solutions Division, NEC Corporation, Tokyo, Japan

7Future University Hakodate, Tokyo, Japan

8Hokkai Gakuen University, Sapporo, Japan

*Corresponding Author:
Yamazaki T
Department of Bioscience and Bioinformatics
Kyushu Institute of Technology
Kawazu, Iizuka, Fukuoka, Japan
Tel: +81-948-29-7818
E-mail: [email protected]

Received Date: August 18, 2015; Accepted Date: August 27, 2015; Published Date: August 31, 2015

Citation: Yamaguchi H, Yamazaki T, Yamamoto K, Ueno S, Yamaguchi A, et al. (2015) Decoding Silent Speech in Japanese from Single Trial EEGS: Preliminary Results. J Comput Sci Syst Biol 08:285-291. doi:10.4172/jcsb.1000202

Copyright: © 2015 Yamaguchi H, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

We propose a new scheme for speaker-dependent silent speech recognition systems (SSRSs) using both single-trial electroencephalograms (EEGs) scalp-recorded and speech signals measured during overtly and covertly speaking “janken” and “season” in Japanese. This scheme consists of two phases. The learning phase specifies a Kalman filter using spectrograms of the speech signals and independent components (ICs), whose equivalent current dipole source localization (ECDL) solutions were located mainly at the Broca’s area, of the EEGs during the actual speech. In case of the “season” task, the speech signals were transformed into vowel and consonant sequences, and these relationships were learned by hidden Markov model (HMM) with Gaussian mixture densities. The decoding phase predicts spectrograms for the silent “janken” and “season” using the Kalman filter with the EEGs during the silent speech. For the silent “season”, the predicted spectrograms were inputted to the HMM, and which “season” was silently spoken was determined by the maximal log-likelihood among each HMM. Our preliminary results as training steps are as follows: the silent “jankens” were correctly discriminated; the silent “season”-HMMs worked well, suggesting that this scheme might be applied to the discrimination between all the pairs of the hiraganas

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