Thu-2-9-6 Improving the Prosody of RNN-based English Text-To-Speech Synthesis by Incorporating a BERT model

Tom Kenter(Google UK), Manish Sharma(Google) and Robert Clark(Google, UK)
Abstract: The prosody of currently available speech synthesis systems can be unnatural due to the systems only having access to the text, possibly enriched by linguistic information such as part- of-speech tags and parse trees. We show that incorporating a BERT model in an RNN-based speech synthesis model — where the BERT model is pretrained on large amounts of un- labeled data, and fine-tuned to the speech domain — improves prosody. Additionally, we propose a way of handling arbitrarily long sequences with BERT. Our findings indicate that small BERT models work better than big ones, and that fine-tuning the BERT part of the model is pivotal for getting good results.
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