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.

翻译：基于Transformer的模型近年来在端到端自动语音识别应用中取得了显著进展。借助Transformer模型，可将端到端语音识别系统部署至智能设备，但这类模型仍存在参数量过大的缺陷。为克服通用Transformer模型在边缘设备语音识别应用中的不足，我们提出了一种适用于小规模语音识别系统的Transformer块复用方案，该方案在满足资源限制的同时保持识别精度。具体而言，我们设计了面向语音Transformer的块复用策略（BRST）以提升参数效率，并提出适配器模块（ADM），该模块仅通过为每个复用块添加少量可训练参数即可生成紧凑且可适配的模型。在公开AISHELL-1语料库上的实验表明，所提方法在不使用/使用ADM时分别以7.6M/8.3M参数实现了9.3%/6.63%的字错误率。此外，我们通过更深入的分析揭示了ADM在通用块复用方法中的作用。