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Voice conversion systems have made significant advancements in terms ofnaturalness and similarity in common voice conversion tasks. However, theirperformance in more complex tasks such as cross-lingual voice conversion andexpressive voice conversion remains imperfect. In this study, we propose anovel approach that combines a jointly trained speaker encoder and contentfeatures extracted from the cross-lingual speech recognition model Whisper toachieve high-quality cross-lingual voice conversion. Additionally, we introducea speaker consistency loss to the joint encoder, which improves the similaritybetween the converted speech and the reference speech. To further explore thecapabilities of the joint speaker encoder, we use the phonetic posteriorgram asthe content feature, which enables the model to effectively reproduce both thespeaker characteristics and the emotional aspects of the reference speech.

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