Improving severity preservation of healthy-to-pathological voice conversion with global style tokens

Autor:	Halpern, Bence Mark, Huang, Wen-Chin, Violeta, Lester Phillip, van Son, R. J. J. H., Toda, Tomoki
Rok vydání:	2023
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing I.2.7
Druh dokumentu:	Working Paper
Popis:	In healthy-to-pathological voice conversion (H2P-VC), healthy speech is converted into pathological while preserving the identity. The paper improves on previous two-stage approach to H2P-VC where (1) speech is created first with the appropriate severity, (2) then the speaker identity of the voice is converted while preserving the severity of the voice. Specifically, we propose improvements to (2) by using phonetic posteriorgrams (PPG) and global style tokens (GST). Furthermore, we present a new dataset that contains parallel recordings of pathological and healthy speakers with the same identity which allows more precise evaluation. Listening tests by expert listeners show that the framework preserves severity of the source sample, while modelling target speaker's voice. We also show that (a) pathology impacts x-vectors but not all speaker information is lost, (b) choosing source speakers based on severity labels alone is insufficient. Comment: 7 pages, 3 figures, 5 tables. Accepted to IEEE Automatic Speech Recognition and Understanding Workshop 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.02570 Zobrazit plný text záznamu View this record from Arxiv