Baidu USA LLC

Heewoo Jun

Sanjeev Satheesh

Adam Coates

Anuroop Sriram

Zhenyao Zhu

Eric Dean Battenberg

Christopher Fougner

Hairong Liu

Markus Kliegl

Vinay Rao

Ajay Kannan

Rewon Child

Yashesh Gaur

Jiaji Huang

Atul Kumar

David Seetapun

Described herein are systems and methods for generating natural language sentences with Sequence-to-sequence (Seq2Seq) models with attention. The Seq2Seq models may be implemented in applications, such as machine translation, image captioning, and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language models. Disclosed herein are 'Cold Fusion' architecture embodiments that leverage a pre-trained language model during training. The Seq2Seq models with Cold Fusion embodiments are able to better utilize language information enjoying faster convergence, better generalization, and almost complete transfer to a new domain while using less labeled training data.

Cold fusing sequence-to-sequence models with language models

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

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Sunnyvale, CA, United States of America

Heewoo Jun