Using joint training speaker encoder with consistency loss to achieve cross-lingual voice conversion and expressive voice conversion

TL;DR

This paper presents a novel joint training approach with a consistency loss for cross-lingual and expressive voice conversion, leveraging a cross-lingual speech recognition model and phonetic features to improve quality and similarity.

Contribution

It introduces a combined speaker encoder with a consistency loss and uses phonetic posteriorgrams for enhanced cross-lingual and expressive voice conversion.

Findings

Improved voice conversion quality in cross-lingual tasks.

Enhanced speaker similarity and emotional expression reproduction.

Effective use of Whisper model for content extraction.

Abstract

Voice conversion systems have made significant advancements in terms of naturalness and similarity in common voice conversion tasks. However, their performance in more complex tasks such as cross-lingual voice conversion and expressive voice conversion remains imperfect. In this study, we propose a novel approach that combines a jointly trained speaker encoder and content features extracted from the cross-lingual speech recognition model Whisper to achieve high-quality cross-lingual voice conversion. Additionally, we introduce a speaker consistency loss to the joint encoder, which improves the similarity between the converted speech and the reference speech. To further explore the capabilities of the joint speaker encoder, we use the phonetic posteriorgram as the content feature, which enables the model to effectively reproduce both the speaker characteristics and the emotional aspects of the reference speech.