Evaluation of DNN-based Speech Recognition for English Spoken by Japanese Learners

• Item type: SIG Technical Reports(1)
• 公開日: 2018-06-09
• タイトル: Evaluation of DNN-based Speech Recognition for English Spoken by Japanese Learners
• タイトル / 言語: en
• タイトル: Evaluation of DNN-based Speech Recognition for English Spoken by Japanese Learners
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: ポスターセッション
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Engineering, Tohoku University
• 著者所属: Graduate School of Engineering, Tohoku University
• 著者所属: Graduate School of Engineering, Tohoku University
• 著者所属: Graduate School of Engineering, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Tohoku University
• 著者名: Jiang, Fu; Yuya, Chiba; Takashi, Nose; Akinori, Ito
• 著者名(英): Jiang, Fu; Yuya, Chiba; Takashi, Nose; Akinori, Ito
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Regarding the assistance of computer-assisted language learning (CALL) systems to make foreign language learning easier, it is necessary to recognize the utterances of the learner with high accuracy. The quality of CALL systems mainly depends on the accuracy of automatic speech recognition (ASR). However, since pronunciation of non-native speakers is greatly different from that of native speakers, existing ASR system cannot well recognize speech accurately. To solve this problem, this research projects an acoustic model based on deep neural networks (DNN), which is trained by using ERJ (English Read by Japanese) database collected from 202 Japanese learners. Compared with traditional ASR systems, this new system significantly promotes speech recognition rate.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Regarding the assistance of computer-assisted language learning (CALL) systems to make foreign language learning easier, it is necessary to recognize the utterances of the learner with high accuracy. The quality of CALL systems mainly depends on the accuracy of automatic speech recognition (ASR). However, since pronunciation of non-native speakers is greatly different from that of native speakers, existing ASR system cannot well recognize speech accurately. To solve this problem, this research projects an acoustic model based on deep neural networks (DNN), which is trained by using ERJ (English Read by Japanese) database collected from 202 Japanese learners. Compared with traditional ASR systems, this new system significantly promotes speech recognition rate.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10438388
• 書誌情報: 研究報告音楽情報科学（MUS）; 巻 2018-MUS-119, 号 26, p. 1-5, 発行日 2018-06-09
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8752
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会