Baidu USA LLC

Mike Chrzanowski

Xu Han

Sercan Omer Arik

Gregory Diamos

Bryan Catanzaro

Jun Zhan

Ryan Prenger

Sanjeev Satheesh

Adam Coates

Jesse Engel

Jonathan Raiman

Erich Elsen

Zhenyao Zhu

Andrew Gibiansky

John Miller

Shubhabrata Sengupta

Christopher Fougner

Chong Wang

Awni Hannun

Andrew Ng

Mohammad Shoeybi

Dario Amodei

Jingdong Chen

Shubhahrata Sengupta

Dani Yogatama

Embodiments of a production-quality text-to-speech (TTS) system constructed from deep neural networks are described. System embodiments comprise five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-to-phoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model. For embodiments of the segmentation model, phoneme boundary detection was performed with deep neural networks using Connectionist Temporal Classification (CTC) loss. For embodiments of the audio synthesis model, a variant of WaveNet was created that requires fewer parameters and trains faster than the original. By using a neural network for each component, system embodiments are simpler and more flexible than traditional TTS systems, where each component requires laborious feature engineering and extensive domain expertise. Inference with system embodiments may be performed faster than real time.

Real-time neural text-to-speech

Systems and methods for real-time neural text-to-speech

Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

End-to-end speech recognition

Deployed end-to-end speech recognition

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Sunnyvale, CA, United States of America

Mike Chrzanowski