  Continuous integrate-and-fire (CIF) based models, which use a soft and
monotonic alignment mechanism, have been well applied in non-autoregressive
(NAR) speech recognition and achieved competitive performance compared with
other NAR methods. However, such an alignment learning strategy may also result
in inaccurate acoustic boundary estimation and deceleration in convergence
speed. To eliminate these drawbacks and improve performance further, we
incorporate an additional connectionist temporal classification (CTC) based
alignment loss and a contextual decoder into the CIF-based NAR model.
Specifically, we use the CTC spike information to guide the leaning of acoustic
boundary and adopt a new contextual decoder to capture the linguistic
dependencies within a sentence in the conventional CIF model. Besides, a
recently proposed Conformer architecture is also employed to model both local
and global acoustic dependencies. Experiments on the open-source Mandarin
corpora AISHELL-1 show that the proposed method achieves a comparable character
error rate (CER) of 4.9% with only 1/24 latency compared with a
state-of-the-art autoregressive (AR) Conformer model.
