  Recently, sequence-to-sequence (seq-to-seq) models have been successfully
applied in text-to-speech (TTS) to synthesize speech for single-language text.
To synthesize speech for multiple languages usually requires multi-lingual
speech from the target speaker. However, it is both laborious and expensive to
collect high-quality multi-lingual TTS data for the target speakers. In this
paper, we proposed to use low-quality code-switched found data from the
non-target speakers to achieve cross-lingual voice cloning for the target
speakers. Experiments show that our proposed method can generate high-quality
code-switched speech in the target voices in terms of both naturalness and
speaker consistency. More importantly, we find that our method can achieve a
comparable result to the state-of-the-art (SOTA) performance in cross-lingual
voice cloning.
