  Recent advancements in end-to-end speech synthesis have made it possible to
generate highly natural speech. However, training these models typically
requires a large amount of high-fidelity speech data, and for unseen texts, the
prosody of synthesized speech is relatively unnatural. To address these issues,
we propose to combine a fine-tuned BERT-based front-end with a pre-trained
FastSpeech2-based acoustic model to improve prosody modeling. The pre-trained
BERT is fine-tuned on the polyphone disambiguation task, the joint Chinese word
segmentation (CWS) and part-of-speech (POS) tagging task, and the prosody
structure prediction (PSP) task in a multi-task learning framework. FastSpeech
2 is pre-trained on large-scale external data that are noisy but easier to
obtain. Experimental results show that both the fine-tuned BERT model and the
pre-trained FastSpeech 2 can improve prosody, especially for those structurally
complex sentences.
