In the last decade of automatic speech recognition (ASR) research, the introduction of deep learning brought considerable reductions in word error rate of more than 50% relative, compared to modeling without deep learning. In the wake of this transition, a number of all-neural ASR architectures were introduced. These so-called end-to-end (E2E) models provide highly integrated, completely neural ASR models, which rely strongly on general machine learning knowledge, learn more consistently from data, while depending less on ASR domain-specific experience. The success and enthusiastic adoption of deep learning accompanied by more generic model architectures lead to E2E models now becoming the prominent ASR approach. The goal of this survey is to provide a taxonomy of E2E ASR models and corresponding improvements, and to discuss their properties and their relation to the classical hidden Markov model (HMM) based ASR architecture. All relevant aspects of E2E ASR are covered in this work: modeling, training, decoding, and external language model integration, accompanied by discussions of performance and deployment opportunities, as well as an outlook into potential future developments.

翻译：在自动语音识别（ASR）研究的过去十年中，深度学习的引入使得词错误率相比未使用深度学习的建模方法显著降低了50%以上。在这一转变的推动下，一系列全神经架构的ASR系统随之出现。这些所谓的端到端（E2E）模型提供了高度集成、完全基于神经网络的ASR模型，其强烈依赖于通用的机器学习知识，能够更一致地从数据中学习，同时较少依赖ASR领域的专业经验。深度学习的成功及其伴随更通用模型架构的广泛采用，使得E2E模型如今已成为主流的ASR方法。本综述的目标是提出E2E ASR模型及其相应改进的分类体系，并讨论其特性以及与基于经典隐马尔可夫模型（HMM）的ASR架构之间的关系。本文涵盖了E2E ASR的所有相关方面：建模、训练、解码以及外部语言模型集成，同时附带了关于性能与部署机会的讨论，并对潜在未来发展方向进行了展望。