Domain Adaptation and Language Conditioning to Improve Phonetic Posteriorgram Based Cross-Lingual Voice Conversion

In this work, we examine two methods for im-proving phonetic posteriorgram (PPG) based cross-lingual voice conversion (CLV C). Previous research usually utilized a speaker encoder to characterize speakers' identity; however, the speaker embedding learned by the previous model tends to be language- dependent, degrading the performance of converted speeches. Therefore, we propose using the technique of domain-adversarial training. With this approach, the speaker embedding in different languages can be adapted into the same distribution to form a language-independent speaker embedding space. The other approach we propose is to employ external language conditioning to support our model to disentangle the language information from the speaker embedding. In our experiments, both methods are evaluated on a Japanese-English bilingual database. Besides subjective evaluation, two automatic objective assessment systems are adopted to assess the quality and speaker similarity of converted utterances. According to the experimental results, the two proposed methods can generate speaker embedding with reduced language dependency and improve the naturalness and speaker similarity of converted speeches.