  The success of self-attention in NLP has led to recent applications in
end-to-end encoder-decoder architectures for speech recognition. Separately,
connectionist temporal classification (CTC) has matured as an alignment-free,
non-autoregressive approach to 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
end-to-end speech recognition. SAN-CTC trains quickly and outperforms existing
CTC models and most encoder-decoder models, with character error rates (CERs)
of 4.7% in 1 day on WSJ eval92 and 2.8% in 1 week on LibriSpeech test-clean,
with a fixed architecture and one GPU. Similar improvements hold for WERs after
LM decoding. We motivate the architecture for speech, evaluate position and
downsampling approaches, and explore how label alphabets (character, phoneme,
subword) affect attention heads and performance.
