Transformer-based models have recently made significant achievements in the
application of end-to-end (E2E) automatic speech recognition (ASR). It is
possible to deploy the E2E ASR system on smart devices with the help of
Transformer-based models. While these models still have the disadvantage of
requiring a large number of model parameters. To overcome the drawback of
universal Transformer models for the application of ASR on edge devices, we
propose a solution that can reuse the block in Transformer models for the
occasion of the small footprint ASR system, which meets the objective of
accommodating resource limitations without compromising recognition accuracy.
Specifically, we design a novel block-reusing strategy for speech Transformer
(BRST) to enhance the effectiveness of parameters and propose an adapter module
(ADM) that can produce a compact and adaptable model with only a few additional
trainable parameters accompanying each reusing block. We conducted an
experiment with the proposed method on the public AISHELL-1 corpus, and the
results show that the proposed approach achieves the character error rate (CER)
of 9.3%/6.63% with only 7.6M/8.3M parameters without and with the ADM,
respectively. In addition, we also make a deeper analysis to show the effect of
ADM in the general block-reusing method.

本文提出了一种基于块重复策略和适配器模块的解决方案 (BRST 和 ADM)，旨在适应资源限制的小型自动语音识别（ASR）系统，并在公共语料库实验中取得了较好的识别结果。