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End-to-end (E2E) automatic speech recognition (ASR) models, by now, haveshown competitive performance on several benchmarks. These models arestructured to either operate in streaming or non-streaming mode. This workpresents cascaded encoders for building a single E2E ASR model that can operatein both these modes simultaneously. The proposed model consists of streamingand non-streaming encoders. Input features are first processed by the streamingencoder; the non-streaming encoder operates exclusively on the output of thestreaming encoder. A single decoder then learns to decode either using theoutput of the streaming or the non-streaming encoder. Results show that thismodel achieves similar word error rates (WER) as a standalone streaming modelwhen operating in streaming mode, and obtains 10% -- 27% relative improvementwhen operating in non-streaming mode. Our results also show that the proposedapproach outperforms existing E2E two-pass models, especially on long-formspeech.

Cascaded encoders for unifying streaming and non-streaming ASR