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。

超越通用 Transformer: 在 Transformer 中使用适配器进行块重复以进行自动语音识别

Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognit

This paper investigates whether learning contingency-awareness and
controllable aspects of an environment can lead to better exploration in
reinforcement learning. To investigate this question, we consider an
instantiation of this hypothesis evaluated on the Arcade Learning Element
(ALE). In this study, we develop an attentive dynamics model (ADM) that
discovers controllable elements of the observations, which are often associated
with the location of the character in Atari games. The ADM is trained in a
self-supervised fashion to predict the actions taken by the agent. The learned
contingency information is used as a part of the state representation for
exploration purposes. We demonstrate that combining actor-critic algorithm with
count-based exploration using our representation achieves impressive results on
a set of notoriously challenging Atari games due to sparse rewards. For
example, we report a state-of-the-art score of >11,000 points on Montezuma's
Revenge without using expert demonstrations, explicit high-level information
(e.g., RAM states), or supervisory data. Our experiments confirm that
contingency-awareness is indeed an extremely powerful concept for tackling
exploration problems in reinforcement learning and opens up interesting
research questions for further investigations.

本文研究了学习环境的可控方面和连续性感知是否能够导致强化学习中更好的探索并开展了相关实验，结果表明使用我们的态势代表结合演员 - 评论家算法和计数探索实现了卓越的效果。