機械学習による表面筋電信号を用いた日本語単音の認識

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2023/05/01

タイトル(英語): Recognition of Single Japanese Sounds using Surface Electromyography Signals by Machine Learning

著者名: 神倉　怜（大阪公立大学大学院工学研究科），村治　雅文（大阪公立大学大学院工学研究科），白藤　立（大阪公立大学大学院工学研究科）

著者名(英語): Rei Kamikura (Department of Physical Electronics & Information, Graduate School of Engineering, Osaka Metropolitan University), Masafumi Muraji (Department of Physical Electronics & Information, Graduate School of Engineering, Osaka Metropolitan University), Tatsuru Shirafuji (Department of Physical Electronics & Information, Graduate School of Engineering, Osaka Metropolitan University)

キーワード: 筋電信号，sEMG信号，日本語単音，認識，機械学習，ニューラルネットワーク　　electromyography，sEMG signals，single Japanese sounds，recognition，machine learning，neural network

要約(英語): The method and results of recognizing single Japanese sounds using surface electromyography (sEMG) signals generated from muscles around the mouth are presented. We determined six features of the waveforms of four muscles (a total of 24 indexes) and recognized 45 single Japanese sounds. We used machine learning with a neural network to improve sound recognition. The neural network has a 24 node input layer, a 100 node intermediate layer, and a 45 node output layer. Each index of a sound was entered into the input layer; the probabilities of the sound were output to the output layer. They were compared, and the output (sound) with the highest probability was determined to be a recognition sound. We used the cross-entropy loss as the loss function and gradient descent as the machine learning method. Machine learning, which built a neural network, has dramatically increased recognition; it stands at approximately 94%.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.5 （2023） 特集：医用・生体工学関連技術

本誌掲載ページ: 527-531 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/5/143_527/_article/-char/ja/