| Presentation | 2018-01-20 [Poster Presentation] A study on the articulatory-to-speech conversion by using deep learning Fumiaki Taguchi, Tokihiko Kaburagi, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this study, we examined a method to convert the movement pattern of articulatory organs observed by a magnetic sensor (EMA) into feature parameters of speech. In conventional studies, articulation parameters representing movement pattern of articulatory organs were usually converted to feature parameters representing the spectral envelope of the speech, because articulation parameters are directly related to the acoustic characteristics of the vocal tract. However, articulatory parameters and the acoustic characteristics of the vocal tract are responsible for the phonological properties of speech and phonemic information is related to glottal sound source information such as the pitch pattern and the voiced-unvoiced distinction. These considerations suggest that there exists a certain kind of relationship between articulatory parameters and the glottal sound source information. In this study, we relied on this relationship and estimated not only the spectral envelope but also features related to the glottal sound source, thereby synthesizing speech directly from the movement orbit of articulatory organs. We also objectively evaluated the estimation accuracy of speech parameters. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | articulatory movement / vocal tract spectrum / Deep Learning / articulatory-to-acoustic mapping |
| Paper # | SP2017-70 |
| Date of Issue | 2018-01-13 (SP) |
| Conference Information | |
| Committee | SP / ASJ-H |
|---|---|
| Conference Date | 2018/1/20(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | The University of Tokyo |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Yoichi Yamashita(Ritsumeikan Univ.) / 平原 達也(富山県立大) |
| Vice Chair | Hiroki Mori(Utsunomiya Univ.) / 中川 誠司(千葉大) |
| Secretary | Hiroki Mori(Shizuoka Univ.) / 中川 誠司(Meijo Univ.) |
| Assistant | Kei Hashimoto(Nagoya Inst. of Tech.) / Satoshi Kobashikawa(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Speech / Auditory Research Meeting |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Poster Presentation] A study on the articulatory-to-speech conversion by using deep learning |
| Sub Title (in English) | |
| Keyword(1) | articulatory movement |
| Keyword(2) | vocal tract spectrum |
| Keyword(3) | Deep Learning |
| Keyword(4) | articulatory-to-acoustic mapping |
| 1st Author's Name | Fumiaki Taguchi |
| 1st Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 2nd Author's Name | Tokihiko Kaburagi |
| 2nd Author's Affiliation | Kyushu University(Kyushu Univ.) |
| Date | 2018-01-20 |
| Paper # | SP2017-70 |
| Volume (vol) | vol.117 |
| Number (no) | SP-393 |
| Page | pp.pp.27-30(SP), |
| #Pages | 4 |
| Date of Issue | 2018-01-13 (SP) |