Item type |
SIG Technical Reports(1) |
公開日 |
2017-02-10 |
タイトル |
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タイトル |
DNN-based GOP and Its Application to Automatic Assessment of Shadowing Speeches |
タイトル |
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言語 |
en |
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タイトル |
DNN-based GOP and Its Application to Automatic Assessment of Shadowing Speeches |
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言語 |
eng |
キーワード |
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主題Scheme |
Other |
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主題 |
音声認識・応用 |
資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
著者所属 |
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The University of Tokyo |
著者所属 |
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The University of Tokyo |
著者所属 |
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The University of Tokyo |
著者所属 |
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Tokyo International University |
著者所属 |
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Kyoto University |
著者所属 |
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The University of Tokyo |
著者所属 |
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The University of Tokyo |
著者所属(英) |
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en |
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The University of Tokyo |
著者所属(英) |
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en |
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The University of Tokyo |
著者所属(英) |
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en |
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The University of Tokyo |
著者所属(英) |
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en |
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Tokyo International University |
著者所属(英) |
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en |
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Kyoto University |
著者所属(英) |
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en |
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The University of Tokyo |
著者所属(英) |
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en |
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The University of Tokyo |
著者名 |
Junwei, Yue
Fumiya, Shiozawa
Shohei, Toyama
Yutaka, Yamauchi
Kayoko, Ito
Daisuke, Saito
Nobuaki, Minematsu
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著者名(英) |
Junwei, Yue
Fumiya, Shiozawa
Shohei, Toyama
Yutaka, Yamauchi
Kayoko, Ito
Daisuke, Saito
Nobuaki, Minematsu
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論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
Shadowing is currently one of the most popular research topics in CALL (Computer Assisted Language Learning). Our previous studies realized automatic assessment using the GOP (Goodness of Pronunciation) scores, and made a step toward automatically generating corrective feedbacks for shadowing speeches. In this study, we collected English shadowing speeches from Japanese university students. Manual scores of these speeches are given by a bilingual English teacher. Using this labeled corpus, we investigated automatic proficiency assessment using DNN (Deep Neural Network) based acoustic models. Here GOP (Goodness of Pronunciation) scores were estimated using DNN and they were compared to GMM-based GOP scores in terms of assessment performance. Further, DTW (Dynamic Time Wrapping) distances between learners' shadowed utterances and model utterances were calculated using posterior vectors. This DTW-based score was also compared to GOP-based scores. The result suggests that DNN based approach shows better performance than traditional GMM based ones. In the DTW-based comparison, language independency was also discussed. |
論文抄録(英) |
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内容記述タイプ |
Other |
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内容記述 |
Shadowing is currently one of the most popular research topics in CALL (Computer Assisted Language Learning). Our previous studies realized automatic assessment using the GOP (Goodness of Pronunciation) scores, and made a step toward automatically generating corrective feedbacks for shadowing speeches. In this study, we collected English shadowing speeches from Japanese university students. Manual scores of these speeches are given by a bilingual English teacher. Using this labeled corpus, we investigated automatic proficiency assessment using DNN (Deep Neural Network) based acoustic models. Here GOP (Goodness of Pronunciation) scores were estimated using DNN and they were compared to GMM-based GOP scores in terms of assessment performance. Further, DTW (Dynamic Time Wrapping) distances between learners' shadowed utterances and model utterances were calculated using posterior vectors. This DTW-based score was also compared to GOP-based scores. The result suggests that DNN based approach shows better performance than traditional GMM based ones. In the DTW-based comparison, language independency was also discussed. |
書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AN10442647 |
書誌情報 |
研究報告音声言語情報処理(SLP)
巻 2017-SLP-115,
号 13,
p. 1-6,
発行日 2017-02-10
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ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8663 |
Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |