  Cross-lingual voice conversion (VC) is a task that aims to synthesize target
voices with the same content while source and target speakers speak in
different languages. Its challenge lies in the fact that the source and target
data are naturally non-parallel, and it is even difficult to bridge the gaps
between languages with no transcriptions provided. In this paper, we focus on
knowledge transfer from monolin-gual ASR to cross-lingual VC, in order to
address the con-tent mismatch problem. To achieve this, we first train a
monolingual acoustic model for the source language, use it to extract phonetic
features for all the speech in the VC dataset, and then train a Seq2Seq
conversion model to pre-dict the mel-spectrograms. We successfully address
cross-lingual VC without any transcription or language-specific knowledge for
foreign speech. We experiment this on Voice Conversion Challenge 2020 datasets
and show that our speaker-dependent conversion model outperforms the zero-shot
baseline, achieving MOS of 3.83 and 3.54 in speech quality and speaker
similarity for cross-lingual conversion. When compared to Cascade ASR-TTS
method, our proposed one significantly reduces the MOS drop be-tween intra- and
cross-lingual conversion.
