Baidu USA LLC

Erich Eisen

Gregory Diamos

Bryan Catanzaro

Ryan Prenger

Sanjeev Satheesh

Adam Coates

Awni Hannun

Shubhabrata Sengupta

Andrew Ng

Carl Case

Jared Casper

Presented herein are embodiments of state-of-the-art speech recognition systems developed using end-to-end deep learning. In embodiments, the model architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, embodiments of the system do not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learn a function that is robust to such effects. Neither a phoneme dictionary, nor even the concept of a 'phoneme,' is needed. Embodiments include a well-optimized recurrent neural network (RNN) training system that can use multiple GPUs, as well as a set of novel data synthesis techniques that allows for a large amount of varied data for training to be efficiently obtained. Embodiments of the system can also handle challenging noisy environments better than widely used, state-of-the-art commercial speech systems.

Deep learning models for speech recognition

Growing community of inventors

Mountain View, CA, United States of America

Erich Eisen