  Transducer-based models, such as RNN-Transducer and transformer-transducer,
have achieved great success in speech recognition. A typical transducer model
decodes the output sequence conditioned on the current acoustic state and
previously predicted tokens step by step. Statistically, The number of blank
tokens in the prediction results accounts for nearly 90\% of all tokens. It
takes a lot of computation and time to predict the blank tokens, but only the
non-blank tokens will appear in the final output sequence. Therefore, we
propose a method named fast-skip regularization, which tries to align the blank
position predicted by a transducer with that predicted by a CTC model. During
the inference, the transducer model can predict the blank tokens in advance by
a simple CTC project layer without many complicated forward calculations of the
transducer decoder and then skip them, which will reduce the computation and
improve the inference speed greatly. All experiments are conducted on a public
Chinese mandarin dataset AISHELL-1. The results show that the fast-skip
regularization can indeed help the transducer model learn the blank position
alignments. Besides, the inference with fast-skip can be speeded up nearly 4
times with only a little performance degradation.

Audio and Speech Processing;Computation and Language;Sound