Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review
This review addresses the problem of understanding and advancing code-switching ASR for researchers and practitioners, but it is incremental as it synthesizes existing work without new methods or results.
The authors conducted a systematic literature review on code-switching in end-to-end automatic speech recognition, documenting languages, datasets, metrics, model choices, and performance to analyze current research and identify gaps.
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.