  This paper proposes RefXVC, a method for cross-lingual voice conversion (XVC)
that leverages reference information to improve conversion performance.
Previous XVC works generally take an average speaker embedding to condition the
speaker identity, which does not account for the changing timbre of speech that
occurs with different pronunciations. To address this, our method uses both
global and local speaker embeddings to capture the timbre changes during speech
conversion. Additionally, we observed a connection between timbre and
pronunciation in different languages and utilized this by incorporating a
timbre encoder and a pronunciation matching network into our model.
Furthermore, we found that the variation in tones is not adequately reflected
in a sentence, and therefore, we used multiple references to better capture the
range of a speaker's voice. The proposed method outperformed existing systems
in terms of both speech quality and speaker similarity, highlighting the
effectiveness of leveraging reference information in cross-lingual voice
conversion. The converted speech samples can be found on the website:
\url{http://refxvc.dn3point.com}
