Abstract / Description of output
An audiovisual speaker conversion method is presented for simultaneously transforming the facial expressions and voice of a source speaker into those of a target speaker. Transforming the facial and acoustic features together makes it possible for the converted voice and facial expressions to be highly correlated and for the generated target speaker to appear and sound natural. It uses three neural networks: a conversion network that fuses and transforms the facial and acoustic features, a waveform generation network that produces the waveform from both the converted facial and acoustic features, and an image reconstruction network that outputs an RGB facial image also based on both the converted features. The results of experiments using an emotional audiovisual database showed that the proposed method achieved significantly higher naturalness compared with one that separately transformed acoustic and facial features.
Original language | English |
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Title of host publication | ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Place of Publication | Brighton, United Kingdom |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 6795-6799 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-4799-8131-1 |
ISBN (Print) | 978-1-4799-8132-8 |
DOIs | |
Publication status | Published - 17 May 2019 |
Event | 44th International Conference on Acoustics, Speech, and Signal Processing: Signal Processing: Empowering Science and Technology for Humankind - Brighton , United Kingdom Duration: 12 May 2019 → 17 May 2019 Conference number: 44 https://2019.ieeeicassp.org/ |
Publication series
Name | |
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Publisher | IEEE |
ISSN (Print) | 1520-6149 |
ISSN (Electronic) | 2379-190X |
Conference
Conference | 44th International Conference on Acoustics, Speech, and Signal Processing |
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Abbreviated title | ICASSP 2019 |
Country/Territory | United Kingdom |
City | Brighton |
Period | 12/05/19 → 17/05/19 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- Audiovisual speaker conversion
- multi-modality transformation
- machine learning