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BERT, can HE predict contrastive focus? Predicting and controlling prominence in neural TTS using a language model
We collect a corpus of utterances containing contrastive focus and we evaluate the accuracy of a BERT model, finetuned to predict quantized acoustic prominence features, on these samples.
Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input
The prosody of a spoken word is determined by its surrounding context.
What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS
In this paper, we study the behavior of a neural sequence-to-sequence TTS system when used in an incremental mode, i. e. when generating speech output for token n, the system has access to n + k tokens from the text sequence.
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