  In this paper, we propose a novel soft and monotonic alignment mechanism used
for sequence transduction. It is inspired by the integrate-and-fire model in
spiking neural networks and employed in the encoder-decoder framework consists
of continuous functions, thus being named as: Continuous Integrate-and-Fire
(CIF). Applied to the ASR task, CIF not only shows a concise calculation, but
also supports online recognition and acoustic boundary positioning, thus
suitable for various ASR scenarios. Several support strategies are also
proposed to alleviate the unique problems of CIF-based model. With the joint
action of these methods, the CIF-based model shows competitive performance.
Notably, it achieves a word error rate (WER) of 2.86% on the test-clean of
Librispeech and creates new state-of-the-art result on Mandarin telephone ASR
benchmark.
