Amazon Technologies, Inc.

Vatsal Aggarwal

Andrew Paul Breen

Roberto Barra Chicote

Nishant Prateek

Thomas Edward Merritt

Adam Franciszek Nadolski

Bartosz Putrycz

Javier Gonzalez Hernandez

During text-to-speech processing, a speech model creates synthesized speech that corresponds to input data. The speech model may include an encoder for encoding the input data into a context vector and a decoder for decoding the context vector into spectrogram data. The speech model may further include a voice decoder that receives vocal characteristic data representing a desired vocal characteristic of synthesized speech. The voice decoder may process the vocal characteristic data to determine configuration data, such as weights, for use by the speech decoder.

Text-to-speech processing using input voice characteristic data

During text-to-speech processing, a sequence-to-sequence neural network model may process text data and determine corresponding spectrogram data. A normalizing flow component may then process this spectrogram data to predict corresponding phase data. An inverse Fourier transform may then be performed on the spectrogram and phase data to create an audio waveform that includes speech corresponding to the text.

Synthetic speech processing

A speech model is trained using multi-task learning. A first task may correspond to how well predicted audio matches training audio; a second task may correspond to a metric of perceived audio quality. The speech model may include, during training, layers related to the second task that are discarded at runtime.

Text-to-speech (TTS) processing

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Cambridge, United Kingdom

Vatsal Aggarwal