Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review
Agro, Maha Tufail Kulkarni, Atharva A Kadaoui, Karima Talat, Zeerak Al Darmaki, Hanan
Agro, Maha Tufail
Kulkarni, Atharva A
Kadaoui, Karima
Talat, Zeerak
Al Darmaki, Hanan
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
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.
Citation
M.T. Agro, A.A. Kulkarni, K. Kadaoui, Z. Talat, H. Al Darmaki, "Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review," 2026, pp. 9790-9812.
Source
Conference
The Fifteenth Language Resources and Evaluation Conference (LREC 2026)
Keywords
Subjects
Source
The Fifteenth Language Resources and Evaluation Conference (LREC 2026)
Publisher
ELDA