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Transducer-Based Streaming Deliberation For A Cascaded Encoder Model

Kevin Hu
Ruoming Pang
ICASSP 2022 (2022) (to appear)
Google Scholar


Previous research on deliberation networks has achieved excellent recognition quality. The attention decoder based deliberation models often works as a rescorer to improve first-pass recognition results, and often requires the full first-pass hypothesis for second-pass deliberation. In this work, we propose a streaming transducer-based deliberation model. The joint network of a transducer decoder often consists of inputs from the encoder and the prediction network. We propose to use attention to the first-pass text hypotheses 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, 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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