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Self-attention network, an attention-based feedforward neural network, hasrecently shown the potential to replace recurrent neural networks (RNNs) in avariety of NLP tasks. However, it is not clear if the self-attention networkcould be a good alternative of RNNs in automatic speech recognition (ASR),which processes the longer speech sequences and may have online recognitionrequirements. In this paper, we present a RNN-free end-to-end model:self-attention aligner (SAA), which applies the self-attention networks to asimplified recurrent neural aligner (RNA) framework. We also propose achunk-hopping mechanism, which enables the SAA model to encode on segmentedframe chunks one after another to support online recognition. Experiments ontwo Mandarin ASR datasets show the replacement of RNNs by the self-attentionnetworks yields a 8.4%-10.2% relative character error rate (CER) reduction. Inaddition, the chunk-hopping mechanism allows the SAA to have only a 2.5%relative CER degradation with a 320ms latency. After jointly training with aself-attention network language model, our SAA model obtains further error ratereduction on multiple datasets. Especially, it achieves 24.12% CER on theMandarin ASR benchmark (HKUST), exceeding the best end-to-end model by over 2%absolute CER.

Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping