系统梳理代码切换在端到端语音识别中的研究现状与挑战
Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review
- 系统收集并人工标注了同行评审论文，涵盖语言、数据集、模型与评估指标
- 总结现有研究在多语言代码切换场景下的性能表现与技术选择
- 揭示当前研究的资源缺口与关键挑战，为后续工作提供方向
受自动语音识别（ASR）研究兴趣增长以及频繁出现代码切换（CS）语言的推动，本文对端到端ASR模型中的代码切换问题进行了系统性文献综述。我们收集并手动标注了发表于同行评审会议和期刊的论文，记录了涉及的语言、数据集、评估指标、模型选择及性能表现，并讨论了端到端ASR在代码切换场景下面临的挑战。本分析为当前研究进展、可用资源以及未来研究的机会与空白提供了深入洞察。
原文摘要 · Abstract (English)
Motivated by a growing research interest into automatic speech recognition (ASR), and the growing body of work for languages in which code-switching (CS) often occurs, we present a systematic literature review of code-switching in end-to-end ASR models. We collect and manually annotate papers published in peer reviewed venues. We document the languages considered, datasets, metrics, model choices, and performance, and present a discussion of challenges in end-to-end ASR for code-switching. Our analysis thus provides insights on current research efforts and available resources as well as opportunities and gaps to guide future research.
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