语音识别 Transformer：拓扑语言学视角
Speech Recognition Transformers: Topological-lingualism Perspective
AI总结：
本文从拓扑语言学视角全面综述了面向语音模态的 Transformer 技术，涵盖传统 ASR 背景、多语言基础模型、架构与评估指标及工具包，并探讨了开放挑战与未来方向。
摘要：
Transformer 在各种人工智能任务中取得了巨大成功。得益于捕获长程依赖的自注意力机制的普及，语音处理与识别任务产生了显著成果。本文对面向语音模态的 Transformer 技术进行了全面综述。主要内容涵盖：(1) 传统 ASR 背景、端到端 Transformer 生态系统及语音 Transformer；(2) 语言学范式下的语音基础模型，即单语、双语、多语和跨语言；(3) 从特定拓扑语言学视角探讨数据集与语言、声学特征、架构、解码及评估指标；(4) 用于构建端到端 ASR 系统的流行语音 Transformer 工具包。最后，重点讨论了开放性挑战与潜在研究方向，以供学界进一步研究。
英文摘要：
Transformers have evolved with great success in various artificial intelligence tasks. Thanks to our recent prevalence of self-attention mechanisms, which capture long-term dependency, phenomenal outcomes in speech processing and recognition tasks have been produced. The paper presents a comprehensive survey of transformer techniques oriented in speech modality. The main contents of this survey include (1) background of traditional ASR, end-to-end transformer ecosystem, and speech transformers (2) foundational models in a speech via lingualism paradigm, i.e., monolingual, bilingual, multilingual, and cross-lingual (3) dataset and languages, acoustic features, architecture, decoding, and evaluation metric from a specific topological lingualism perspective (4) popular speech transformer toolkit for building end-to-end ASR systems. Finally, highlight the discussion of open challenges and potential research directions for the community to conduct further research in this domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
