  Recent state-of-the-art neural text-to-speech (TTS) synthesis models have
dramatically improved intelligibility and naturalness of generated speech from
text. However, building a good bilingual or code-switched TTS for a particular
voice is still a challenge. The main reason is that it is not easy to obtain a
bilingual corpus from a speaker who achieves native-level fluency in both
languages. In this paper, we explore the use of Mandarin speech recordings from
a Mandarin speaker, and English speech recordings from another English speaker
to build high-quality bilingual and code-switched TTS for both speakers. A
Tacotron2-based cross-lingual voice conversion system is employed to generate
the Mandarin speaker's English speech and the English speaker's Mandarin
speech, which show good naturalness and speaker similarity. The obtained
bilingual data are then augmented with code-switched utterances synthesized
using a Transformer model. With these data, three neural TTS models --
Tacotron2, Transformer and FastSpeech are applied for building bilingual and
code-switched TTS. Subjective evaluation results show that all the three
systems can produce (near-)native-level speech in both languages for each of
the speaker.
