Google Inc.

Isaac Elias

Yonghui Wu

Yu Zhang

Simon Ratner

Ron J Weiss

Igor Kofman

Jasson Schrock

Shanmugavelayutham Muthukrishnan

Byungha Chun

Ye Jia

Julian Frumar

Ryan Junee

Jonathan Shen

Mihai Badoiu

Virginia Wang

Ankur Bhargava

Sigalit Bar

Herbert Ho

Leora Wiseman

Michael Fink

Ran Tavory

Nir Kerem

Aviad Barzilai

A method for training a non-autoregressive TTS model includes receiving training data that includes a reference audio signal and a corresponding input text sequence. The method also includes encoding the reference audio signal into a variational embedding that disentangles the style/prosody information from the reference audio signal and encoding the input text sequence into an encoded text sequence. The method also includes predicting a phoneme duration for each phoneme in the input text sequence and determining a phoneme duration loss based on the predicted phoneme durations and a reference phoneme duration. The method also includes generating one or more predicted mel-frequency spectrogram sequences for the input text sequence and determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence. The method also includes training the TTS model based on the final spectrogram loss and the corresponding phoneme duration loss.

Parallel tacotron non-autoregressive and controllable TTS

A method for training a non-autoregressive TTS model includes obtaining a sequence representation of an encoded text sequence concatenated with a variational embedding. The method also includes using a duration model network to predict a phoneme duration for each phoneme represented by the encoded text sequence. Based on the predicted phoneme durations, the method also includes learning an interval representation and an auxiliary attention context representation. The method also includes upsampling, using the interval representation and the auxiliary attention context representation, the sequence representation into an upsampled output specifying a number of frames. The method also includes generating, based on the upsampled output, one or more predicted mel-frequency spectrogram sequences for the encoded text sequence. The method also includes determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence and training the TTS model based on the final spectrogram loss.

Unsupervised parallel tacotron non-autoregressive and controllable text-to-speech

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Isaac Elias