Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 87 of its 176 references that are in the map. First posted on arXiv 10 Jul 2025.
Builds on
87 of 176 references.
References · 87
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38 more
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50 most cited of 87 All 87 (JSON)
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognitioncited by 4
- 2026Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluationcited by 2
- 2026Contrastive Training with LLM-generated Near-Misses for Robust Code-Switching Speech Recognitioncited by 0
- 2026When Multiple Scripts Matter: Evaluating ASR in Clinical Settingscited by 0
- 2026When Multiple Scripts Matter: Evaluating ASR in Clinical Settings16 Jun 2026
- 2026Contrastive Training with LLM-generated Near-Misses for Robust Code-Switching Speech Recognition5 Jun 2026
- 2026Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation28 May 2026
- 2025HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition29 Sep 2025