Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognition
Shiliang Zhang, Zhifu Gao, Haoneng Luo, Ming Lei, Jie Gao, Zhijie Yan, Lei Xie
Abstract
Recently, streaming end-to-end automatic speech recognition (E2E-ASR) has gained more and more attention.Many efforts have been paid to turn the non-streaming attention-based E2E-ASR system into streaming architecture.In this work, we propose a novel online E2E-ASR system by using Streaming Chunk-Aware Multihead Attention (SCAMA) and a latency control memory equipped self-attention network (LC-SAN-M).LC-SAN-M uses chunk-level input to control the latency of encoder.As to SCAMA, a jointly trained predictor is used to control the output of encoder when feeding to decoder, which enables decoder to generate output in streaming manner.Experimental results on the open 170-hour AISHELL-1 and an industrial-level 20000-hour Mandarin speech recognition tasks show that our approach can significantly outperform the MoChA-based baseline system under comparable setup.On the AISHELL-1 task, our proposed method achieves a character error rate (CER) of 7.39%, to the best of our knowledge, which is the best published performance for online ASR.