End-to-End automatic speech recognition (ASR) models aim to learn a
generalised speech representation to perform recognition. In this domain there
is little research to analyse internal representation dependencies and their
relationship to modelling approaches. This paper investigates cross-domain
language model dependencies within transformer architectures using SVCCA and
uses these insights to exploit modelling approaches. It was found that specific
neural representations within the transformer layers exhibit correlated
behaviour which impacts recognition performance.
Altogether, this work provides analysis of the modelling approaches affecting
contextual dependencies and ASR performance, and can be used to create or adapt
better performing End-to-End ASR models and also for downstream tasks.

分析了 transformer 架构中跨域语言模型依赖关系的研究，使用 SVCCA 发现转换器层中的特定神经表示具有相关行为，并影响识别性能。这项工作提供了有关模型方法的分析，这些模型方法影响了环境依赖关系和 ASR 性能，可以用于创建或调整性能更好的 End-to-End ASR 模型和下游任务。

探究用于端到端语音识别的统计表示

Probing Statistical Representations For End-To-End ASR

Much research in artificial intelligence is concerned with the development of
autonomous agents that can interact effectively with other agents. An important
aspect of such agents is the ability to reason about the behaviours of other
agents, by constructing models which make predictions about various properties
of interest (such as actions, goals, beliefs) of the modelled agents. A variety
of modelling approaches now exist which vary widely in their methodology and
underlying assumptions, catering to the needs of the different sub-communities
within which they were developed and reflecting the different practical uses
for which they are intended. The purpose of the present article is to provide a
comprehensive survey of the salient modelling methods which can be found in the
literature. The article concludes with a discussion of open problems which may
form the basis for fruitful future research.

介绍人工智能领域中对于开发具有自主性并与其它实体有效交互的代理人的方法，重点讨论了不同建模方法及其基础方法学和前提条件，涉及方法学和实际应用，最后阐述了未来研究的潜在课题。