We propose a linear prediction (LP)-based wave-form generation method via WaveNet vocoding framework. A WaveNet-based neural vocoder has significantly improved the quality of parametric text-to-speech (TTS) systems. However, it is challenging to effectively train the neural vocoder when the target database contains massive amount of acoustical information such as prosody, style or expressiveness. As a solution, the approaches that only generate the vocal source component by a neural vocoder have been proposed. However, they tend to generate synthetic noise because the vocal source component is independently handled without considering the entire speech production process; where it is inevitable to come up with a mismatch between vocal source and vocal tract filter. To address this problem, we propose an LP-WaveNet vocoder, where the complicated interactions between vocal source and vocal tract components are jointly trained within a mixture density networkbased WaveNet model. The experimental results verify that the proposed system outperforms the conventional WaveNet vocoders both objectively and subjectively. In particular, the proposed method achieves 4.47 MOS within the TTS framework.
|Title of host publication||2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2020 Dec 7|
|Event||2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand|
Duration: 2020 Dec 7 → 2020 Dec 10
|Name||2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings|
|Conference||2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020|
|Period||20/12/7 → 20/12/10|
Bibliographical noteFunding Information:
VII. ACKNOWLEDGEMENTS The work was supported by Clova Voice, NAVER Corp., Seongnam, Korea, and partially performed when the first author was an intern at Microsoft Research Asia.
© 2020 APSIPA.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Signal Processing
- Decision Sciences (miscellaneous)