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Speech organ contour extraction using real-time MRI and machine learning method
https://doi.org/10.15084/00003036
https://doi.org/10.15084/0000303692fea03b-a911-4e69-9820-18adcc09f22f
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
interspeech_2019_904.pdf (1.2 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2020-10-09 | |||||
| タイトル | ||||||
| タイトル | Speech organ contour extraction using real-time MRI and machine learning method | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | real-time MRI | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | machine learning | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | speech organs | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | articulatory movements | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| ID登録 | ||||||
| ID登録 | 10.15084/00003036 | |||||
| ID登録タイプ | JaLC | |||||
| 著者 |
Takemoto, Hironori
× Takemoto, Hironori× Goto, Tsubasa× Hagihara, Yuya× Hamanaka, Sayaka× Kitamura, Tatsuya× Nota, Yukiko× Maekawa, Kikuo |
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| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Chiba Institute of Technology | |||||
| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Chiba Institute of Technology | |||||
| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Chiba Institute of Technology | |||||
| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Chiba Institute of Technology | |||||
| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Konan University | |||||
| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | National Institute for Japanese Language and Linguistics | |||||
| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | National Institute for Japanese Language and Linguistics | |||||
| 抄録(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Real-time MRI can be used to obtain videos that describe articulatory movements during running speech. For detailed analysis based on a large number of video frames, it is necessary to extract the contours of speech organs, such as the tongue, semi-automatically. The present study attempted to extract the contours of speech organs from videos using a machine learning method. First, an expert operator manually extracted the contours from the frames of a video to build training data sets. The learning operators, or learners, then extracted the contours from each frame of the video. Finally, the errors representing the geometrical distance between the extracted contours and the ground truth, which were the contours excluded from the training data sets, were examined. The results showed that the contours extracted using machine learning were closer to the ground truth than the contours traced by other expert and non-expert operators. In addition, using the same learners, the contours were extracted from other naive videos obtained during different speech tasks of the same subject. As a result, the errors in those videos were similar to those in the video in which the learners were trained. | |||||
| 書誌情報 |
en : Proceedings of Interspeech 2019 p. 904-908, 発行日 2019-09 |
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| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1990-9772 | |||||
| フォーマット | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | application/pdf | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
