Baidu USA LLC

Yashesh Gaur

Sanjeev Satheesh

Anuroop Sriram

Adam Coates

Eric Dean Battenberg

Zhenyao Zhu

Christopher Fougner

Hee Woo Jun

Hairong Liu

Heewoo Jun

Markus Kliegl

Vinay Rao

Ajay Kannan

Rewon Child

David Seetapun

Jiaji Huang

Atul Kumar

Described herein are systems and methods for a general, scalable, end-to-end framework that uses a generative adversarial network (GAN) objective to enable robust speech recognition. Encoders trained with the proposed approach enjoy improved invariance by learning to map noisy audio to the same embedding space as that of clean audio. Embodiments of a Wasserstein GAN framework increase the robustness of seq-to-seq models in a scalable, end-to-end fashion. In one or more embodiments, an encoder component is treated as the generator of GAN and is trained to produce indistinguishable embeddings between labeled and unlabeled audio samples. This new robust training approach can learn to induce robustness without alignment or complicated inference pipeline and even where augmentation of audio data is not possible.

Systems and methods for robust speech recognition using generative adversarial networks

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

Santa Clara, CA, United States of America

Yashesh Gaur