非音声区間拡張マルチコンディション単語モデルの雑音ロバスト性に関する実験的評価

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: 2012/10/01

タイトル(英語): Experimental Evaluation of Noise Robustness for Extended Whole-Word Model with Multi-Condition Training

著者名: 早坂　昇（大阪大学大学院基礎工学研究科），宮永　喜一（北海道大学大学院情報科学研究科）

著者名(英語): Noboru Hayasaka (Graduate School of Engineering Science, Osaka University), Yoshikazu Miyanaga (Graduate School of Information Science and Technology, Hokkaido University)

キーワード: 孤立単語認識，音声区間検出，非音声区間拡張モデル，マルチコンディション学習　　Isolated word recognition，Voice activity detection，Extended whole-word model，Multi-condition training

要約(英語): Voice activity detection (VAD) is an essential technique to develop a sophisticated voice interface. However, VAD with sufficient detection capability has not been presented yet. In particular, it is difficult that the beginning and ending of a word are accurately detected in noisy environments. In this paper, we describe extended models with multi-condition training (extended MC-models) for misdetection and evaluate their noise robustness by a large amount of word recognition simulations. From the results of the simulations, simple whole-word models degraded recognition performance when input speech signal was accompanied by non-speech segments, whereas the extended MC-models maintained the performance. Furthermore, in consideration of practical applications, we carried out the simulations combining CENSREC-1-C baseline VAD with the extended MC-models. The results also showed the usefulness of the extended MC-models under 20 and 10dB signal-to-noise ratio conditions.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.132 No.10 （2012） 特集：医療関連電子技術のニュートピック

本誌掲載ページ: 1667-1674 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/132/10/132_1667/_article/-char/ja/