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Transducer-Based Streaming Deliberation For Cascaded Encoders

Citation Author(s):
Ke Hu, Tara N. Sainath, Arun Narayanan, Ruoming Pang, Trevor Strohman
Submitted by:
Ke Hu
Last updated:
10 May 2022 - 2:06pm
Document Type:
Document Year:
Ke Hu


Previous research on applying deliberation networks to automatic speech recognition has achieved excellent results. The attention decoder based deliberation model often works as a rescorer to improve first-pass recognition results, and requires the full first-pass hypothesis for second-pass deliberation. In this work, we propose a transducer-based streaming deliberation model. The joint network of a transducer decoder often receives inputs from the encoder and the prediction network. We propose to use attention to the first-pass text hypothesis as the third input to the joint network. The proposed transducer based deliberation model naturally streams, making it more desirable for on-device applications. We also show that the model improves rare word recognition compared to cascaded encoders, with relative WER reductions ranging from 3.6% to 10.4% for a variety of test sets. Our model does not use any additional text data for training.

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ICASSP'22 transducer deliberation poster.pdf