Sequence-To-Sequence Voice Conversion using F0 and Time Conditioning and Adversarial Learning - Archive ouverte HAL
Pré-Publication, Document De Travail Année : 2021

Sequence-To-Sequence Voice Conversion using F0 and Time Conditioning and Adversarial Learning

Résumé

This paper presents a sequence-to-sequence voice conversion (S2S-VC) algorithm which allows to preserve some aspects of the source speaker during conversion, typically its prosody, which is useful in many real-life application of voice conversion. In S2S-VC, the decoder is usually conditioned on linguistic and speaker embeddings only, with the consequence that only the linguistic content is actually preserved during conversion. In the proposed S2S-VC architecture, the decoder is conditioned explicitly on the desired F0 sequence so that the converted speech has the same F0 as the one of the source speaker, or any F0 defined arbitrarily. Moreover, an adversarial module is further employed so that the S2S-VC is not only optimized on the available true speech samples, but can also take efficiently advantage of the converted speech samples that can be produced by using various conditioning such as speaker identity, F0, or timing.

Dates et versions

hal-03569597 , version 1 (13-02-2022)

Identifiants

Citer

Frederik Bous, Laurent Benaroya, Nicolas Obin, Axel Roebel. Sequence-To-Sequence Voice Conversion using F0 and Time Conditioning and Adversarial Learning. 2021. ⟨hal-03569597⟩
49 Consultations
0 Téléchargements

Altmetric

Partager

More