  End-to-end models are gaining wider attention in the field of automatic
speech recognition (ASR). One of their advantages is the simplicity of building
that directly recognizes the speech frame sequence into the text label sequence
by neural networks. According to the driving end in the recognition process,
end-to-end ASR models could be categorized into two types: label-synchronous
and frame-synchronous, each of which has unique model behaviour and
characteristic. In this work, we make a detailed comparison on a representative
label-synchronous model (transformer) and a soft frame-synchronous model
(continuous integrate-and-fire (CIF) based model). The results on three public
dataset and a large-scale dataset with 12000 hours of training data show that
the two types of models have respective advantages that are consistent with
their synchronous mode.
