Cascaded encoders for unifying streaming and non-streaming ASR

• URL: http://arxiv.org/abs/2010.14606v1
• Date: Tue, 27 Oct 2020 20:59:50 GMT
• Title: Cascaded encoders for unifying streaming and non-streaming ASR
• Authors: Arun Narayanan, Tara N. Sainath, Ruoming Pang, Jiahui Yu, Chung-Cheng Chiu, Rohit Prabhavalkar, Ehsan Variani, Trevor Strohman
• Abstract summary: This work presents cascaded encoders for building a single E2E ASR model that can operate in both these modes simultaneously. A single decoder then learns to decode either using the output of the streaming or the non-streaming encoder. Results show that this model achieves similar word error rates (WER) as a standalone streaming model when operating in streaming mode, and obtains 10% -- 27% relative improvement when operating in non-streaming mode.
• Score: 68.62941009369125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end (E2E) automatic speech recognition (ASR) models, by now, have shown competitive performance on several benchmarks. These models are structured to either operate in streaming or non-streaming mode. This work presents cascaded encoders for building a single E2E ASR model that can operate in both these modes simultaneously. The proposed model consists of streaming and non-streaming encoders. Input features are first processed by the streaming encoder; the non-streaming encoder operates exclusively on the output of the streaming encoder. A single decoder then learns to decode either using the output of the streaming or the non-streaming encoder. Results show that this model achieves similar word error rates (WER) as a standalone streaming model when operating in streaming mode, and obtains 10% -- 27% relative improvement when operating in non-streaming mode. Our results also show that the proposed approach outperforms existing E2E two-pass models, especially on long-form speech.

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