| Challenge: | Existing studies on code-switching have been limited to the individual languages, but the results are promising. |
| Approach: | They propose to apply linguistic theories to generate more realistic code-switching text, which is needed for language modelling in ASR. |
| Outcome: | The proposed system improves 2% on English-Spanish code-switching . Equivalence Constraint theory and part-of-speech labelling are particularly helpful for text generation and bring 2% improvement to ASR performance. |
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| Challenge: | Code-switching (CS) is a linguistic phenomenon defined as "the alternation of two languages within a single discourse, sentence or constituent." |
| Approach: | They propose an ASR-motivated evaluation setup which is decoupled from an ASL system and the choice of vocabulary . they propose a discriminative training approach which works better than generative language modeling . |
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Automatic Identification of Code-Switching Functions in Speech Transcripts (2023.findings-acl)
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| Challenge: | Code-switching, or switching between languages, occurs for many reasons and has important linguistic, sociological, and cultural implications. |
| Approach: | They build a system to identify a wide range of functions for which speakers code-switch in everyday speech with an accuracy of 75% . they use a dataset of Hindi-English code-witched data to analyze their results . |
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New Datasets and Controllable Iterative Data Augmentation Method for Code-switching ASR Error Correction (2023.findings-emnlp)
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| Challenge: | In bilingual or multilingual settings, code-switching ASR has greater challenges and research value. |
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Improving Language Identification for Code-Switched Speech: The Pivotal Role of Accented English (2026.findings-eacl)
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| Challenge: | Existing models fail to identify English spoken with the accent of the matrix (dominant) language. |
| Approach: | They propose to fine tune existing LID models with accented English to improve code-switched LID . they use a metric that captures relative ranking of identified languages often overlooked by traditional metrics. |
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A Survey of Multilingual Models for Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, but the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. |
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Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages (2020.lrec-1)
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| Challenge: | Existing models for code-switching between languages are under-resourced and limited by text and acoustic data. |
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From English to Code-Switching: Transfer Learning with Strong Morphological Clues (2020.acl-main)
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| Challenge: | Linguistic code-switching (CS) is an understudied area in natural language processing . lack of resources and annotated data makes it difficult to strive for progress in CS-related tasks. |
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CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving (2025.coling-main)
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| Challenge: | More than half of the world's population is presumed to be bilingual . spoken translation of code-switched speech has been under-explored . |
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From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text (2021.acl-long)
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| Challenge: | a computational model for code-switching text is lacking in the corpus of real text. |
| Approach: | They propose a neural machine translation model to generate Hindi-English code-switched sentences using monolingual Hindi sentences. |
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End-to-End Speech Translation for Code Switched Speech (2022.findings-acl)
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Orion Weller, Matthias Sperber, Telmo Pires, Hendra Setiawan, Christian Gollan, Dominic Telaar, Matthias Paulik
| Challenge: | Code switching (CS) is the phenomenon of interchangeably using words and phrases from different languages. |
| Approach: | They propose a new ST corpus that extends the joint transcription and translation setup. |
| Outcome: | The proposed model performs well even when no training data is used. |