July 11, 2018
Learning-based Text To Speech systems have the potential to generalize from one speaker to the next and thus require a relatively short sample of any new voice. However, this promise is currently largely unrealized. We present a method that is designed to capture a new speaker from a short untranscribed audio sample. This is done by employing an additional network that given an audio sample, places the speaker in the embedding space. This network is trained as part of the speech synthesis system using various consistency losses. Our results demonstrate a greatly improved performance on both the dataset speakers, and, more importantly, when fitting new voices, even from very short samples.
Publisher
ICML
Research Topics
April 14, 2024
Heng-Jui Chang, Ning Dong (AI), Ruslan Mavlyutov, Sravya Popuri, Andy Chung
April 14, 2024
March 05, 2024
Alex Liu, Matt Le, Apoorv Vyas, Bowen Shi, Andros Tjandra, Wei-Ning Hsu
March 05, 2024
December 11, 2023
Wei-Ning Hsu, Akinniyi Akinyemi, Alice Rakotoarison, Andros Tjandra, Apoorv Vyas, Baishan Guo, Bapi Akula, Bowen Shi, Brian Ellis, Ivan Cruz, Jeff Wang, Jiemin Zhang, Mary Williamson, Matt Le, Rashel Moritz, Robbie Adkins, William Ngan, Xinyue Zhang, Yael Yungster, Yi-Chiao Wu
December 11, 2023
November 30, 2023
Xutai Ma, Anna Sun, Siqi Ouyang, Hirofumi Inaguma, Paden Tomasello
November 30, 2023
Product experiences
Foundational models
Product experiences
Latest news
Foundational models