  Recent advances in cross-lingual text-to-speech (TTS) made it possible to
synthesize speech in a language foreign to a monolingual speaker. However,
there is still a large gap between the pronunciation of generated cross-lingual
speech and that of native speakers in terms of naturalness and intelligibility.
In this paper, a triplet training scheme is proposed to enhance the
cross-lingual pronunciation by allowing previously unseen content and speaker
combinations to be seen during training. Proposed method introduces an extra
fine-tune stage with triplet loss during training, which efficiently draws the
pronunciation of the synthesized foreign speech closer to those from the native
anchor speaker, while preserving the non-native speaker's timbre. Experiments
are conducted based on a state-of-the-art baseline cross-lingual TTS system and
its enhanced variants. All the objective and subjective evaluations show the
proposed method brings significant improvement in both intelligibility and
naturalness of the synthesized cross-lingual speech.
