Google Inc.

Johns Hopkins University

Yuxuan Wang

Ryan Michael Rifkin

Yonghui Wu

Ron J Weiss

Zhifeng Chen

Navdeep Jaitly

Ioannis Agiomyrgiannakis

Zongheng Yang

Quoc V Le

Samuel Bengio

Robert Andrew James Clark

Russell J Ryan

Ying Xiao

Yu Zhang

Ruoming Pang

Jonathan Shen

Michael Schuster

Russell John Wyatt Skerry-Ryan

Bert Vogelstein

Kenneth W Kinzler

Richard Francis Lyon

Nickolas Papadopoulos

Luis Diaz

Pascal Tom Getreuer

Christopher Thaddeus Hughes

Isaac A Kinde

Chetan Bettegowda

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating speech from text. One of the systems includes one or more computers and one or more storage devices storing instructions that when executed by one or more computers cause the one or more computers to implement: a sequence-to-sequence recurrent neural network configured to: receive a sequence of characters in a particular natural language, and process the sequence of characters to generate a spectrogram of a verbal utterance of the sequence of characters in the particular natural language; and a subsystem configured to: receive the sequence of characters in the particular natural language, and provide the sequence of characters as input to the sequence-to-sequence recurrent neural network to obtain as output the spectrogram of the verbal utterance of the sequence of characters in the particular natural language.

End-to-end text-to-speech conversion

Methods, systems, and computer program products for generating, from an input character sequence, an output sequence of audio data representing the input character sequence. The output sequence of audio data includes a respective audio output sample for each of a number of time steps. One example method includes, for each of the time steps: generating a mel-frequency spectrogram for the time step by processing a representation of a respective portion of the input character sequence using a decoder neural network; generating a probability distribution over a plurality of possible audio output samples for the time step by processing the mel-frequency spectrogram for the time step using a vocoder neural network; and selecting the audio output sample for the time step from the possible audio output samples in accordance with the probability distribution.

Synthesizing speech from text using neural networks

The various implementations described herein include methods, devices, and systems for recognizing speech, such as user commands. In one aspect, a method includes: (1) receiving audio input data via the one or more microphones; (2) generating a plurality of energy channels for the audio input data; (3) generating a feature vector by performing a per-channel normalization to each channel of the plurality of energy channels; and (4) obtaining recognized speech from the audio input utilizing the feature vector.

Systems and methods for recognizing user speech

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Mountain View, CA, United States of America

Yuxuan Wang