Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition
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Self-attention has demonstrated great success in sequence-to-sequence tasks in natural language processing, with preliminary work applying it to end-to-end encoder-decoder approaches in speech recognition. Separately, connectionist temporal classification (CTC) has matured as an alignment-free strategy for monotonic sequence transduction, either by itself or in various multitask and decoding frameworks. We propose SAN-CTC, a deep, fully self-attentional network for CTC, and show it is tractable and competitive for speech recognition. On the Wall Street Journal and LibriSpeech datasets, SAN-CTC trains quickly and outperforms existing CTC models and most encoder-decoder models, attaining 4.7 CER in 1 day and 2.8 and one GPU. We motivate the architecture for speech, evaluate position and downsampling approaches, and explore how the label alphabet affects attention head and performance outcomes.
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