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An inferior performance of the streaming automatic speech recognition modelsversus non-streaming model is frequently seen due to the absence of futurecontext. In order to improve the performance of the streaming model and reducethe computational complexity, a frame-level model using efficient augmentmemory transformer block and dynamic latency training method is employed forstreaming automatic speech recognition in this paper. The long-range historycontext is stored into the augment memory bank as a complement to the limitedhistory context used in the encoder. Key and value are cached by a cachemechanism and reused for next chunk to reduce computation. Afterwards, adynamic latency training method is proposed to obtain better performance andsupport low and high latency inference simultaneously. Our experiments areconducted on benchmark 960h LibriSpeech data set. With an average latency of640ms, our model achieves a relative WER reduction of 6.0% on test-clean and3.0% on test-other versus the truncate chunk-wise Transformer.

Dynamic Latency for CTC-Based Streaming Automatic Speech Recognition With Emformer