  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.
