Cross-Lingual Knowledge Distillation via Flow-Based Voice Conversion for Robust Polyglot Text-to-Speech-Reference-Cited by-同舟云学术

Cross-Lingual Knowledge Distillation via Flow-Based Voice Conversion for Robust Polyglot Text-to-Speech

Published:2023-11-13 Issue: Volume: Page:252-264
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
Short-container-title:

Author:

Piotrowski Dariusz^ORCID,Korzeniowski Renard^ORCID,Falai Alessio^ORCID,Cygert Sebastian^ORCID,Pokora Kamil^ORCID,Tinchev Georgi^ORCID,Zhang Ziyao^ORCID,Yanagisawa Kayoko^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-8126-7_20

Reference31 articles.

1. Bilinski, P., et al.: Creating new voices using normalizing flows. In: Proceedings of Interspeech 2022, pp. 2958–2962 (2022). https://doi.org/10.21437/Interspeech.2022-10195

2. Comini, G., Huybrechts, G., Ribeiro, M.S., Gabryś, A., Lorenzo-Trueba, J.: Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation. In: Proceedings of Interspeech 2022, pp. 1946–1950 (2022). https://doi.org/10.21437/Interspeech.2022-10338

3. Ellinas, N., et al.: Cross-lingual text-to-speech with flow-based voice conversion for improved pronunciation (2022). https://doi.org/10.48550/ARXIV.2210.17264. https://arxiv.org/abs/2210.17264

4. Finkelstein, L., et al.: Training text-to-speech systems from synthetic data: a practical approach for accent transfer tasks. In: Proceedings of Interspeech 2022, pp. 4571–4575 (2022). https://doi.org/10.21437/Interspeech.2022-10115

5. Hwang, M.J., Yamamoto, R., Song, E., Kim, J.M.: TTS-by-TTS: TTS-driven data augmentation for fast and high-quality speech synthesis. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6598–6602 (2021). https://doi.org/10.1109/ICASSP39728.2021.9414408