Baidu USA LLC

Ajay Kannan

Sercan Omer Arik

Wei Ping

Sanjeev Satheesh

Kainan Peng

Adam Coates

Eric Dean Battenberg

Zhenyao Zhu

Andrew Gibiansky

Jonathan Raiman

John Miller

Christopher Fougner

Hairong Liu

Anuroop Sriram

Markus Kliegl

Vinay Rao

Heewoo Jun

Sharan Narang

Yashesh Gaur

Rewon Child

Jiaji Huang

Atul Kumar

David Seetapun

Described herein are embodiments of a fully-convolutional attention-based neural text-to-speech (TTS) system, which various embodiments may generally be referred to as Deep Voice 3. Embodiments of Deep Voice 3 match state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. Deep Voice 3 embodiments were scaled to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, common error modes of attention-based speech synthesis networks were identified and mitigated, and several different waveform synthesis methods were compared. Also presented are embodiments that describe how to scale inference to ten million queries per day on one single-GPU server.

Systems and methods for neural text-to-speech using convolutional sequence learning

Described herein are systems and methods to identify and address sources of bias in an end-to-end speech model. In one or more embodiments, the end-to-end model may be a recurrent neural network with two 2D-convolutional input layers, followed by multiple bidirectional recurrent layers and one fully connected layer before a softmax layer. In one or more embodiments, the network is trained end-to-end using the CTC loss function to directly predict sequences of characters from log spectrograms of audio. With optimized recurrent layers and training together with alignment information, some unwanted bias induced by using purely forward only recurrences may be removed in a deployed model.

Systems and methods for principled bias reduction in production speech models

Growing community of inventors

San Francisco, CA, United States of America

Ajay Kannan