# Adapting Language Balance in Code-Switching Speech

arXiv: https://arxiv.org/abs/2510.18724 · HTML: https://mlsanity.com/paper/2510.18724/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies](https://mlsanity.com/paper/2301.01967/) (2023)
- [ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation](https://mlsanity.com/paper/2112.06223/) (2021)
- [End-to-End Speech Translation for Code Switched Speech](https://mlsanity.com/paper/2204.05076/) (2022)
- [Language-Routing Mixture of Experts for Multilingual and Code-Switching Speech Recognition](https://mlsanity.com/paper/2307.05956/) (2023)
- [A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing](https://mlsanity.com/paper/2210.04675/) (2022)
- [Data Augmentation for End-to-end Code-switching Speech Recognition](https://mlsanity.com/paper/2011.02160/) (2020)
- [Aligning Speech to Languages to Enhance Code-switching Speech Recognition](https://mlsanity.com/paper/2403.05887/) (2024)
- [MaskLID: Code-Switching Language Identification through Iterative Masking](https://mlsanity.com/paper/2406.06263/) (2024)
- [PIER: A Novel Metric for Evaluating What Matters in Code-Switching](https://mlsanity.com/paper/2501.09512/) (2025)
- [Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition](https://mlsanity.com/paper/2201.02550/) (2022)
- [Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models](https://mlsanity.com/paper/2110.03504/) (2021)
- [Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation](https://mlsanity.com/paper/2205.12649/) (2022)
- [An Effective Mixture-Of-Experts Approach For Code-Switching Speech Recognition Leveraging Encoder Disentanglement](https://mlsanity.com/paper/2402.17189/) (2024)
- [Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition](https://mlsanity.com/paper/2210.08992/) (2022)
- [Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition](https://mlsanity.com/paper/2309.11327/) (2023)
- [Weight Factorization and Centralization for Continual Learning in Speech Recognition](https://mlsanity.com/paper/2506.16574/) (2025)
- [SAGE: Spliced-Audio Generated Data for Enhancing Foundational Models in Low-Resource Arabic-English Code-Switched Speech Recognition](https://mlsanity.com/paper/2506.22143/) (2025)

## Built on by (most cited first, 2 of 2)

- [Contrastive Training with LLM-generated Near-Misses for Robust Code-Switching Speech Recognition](https://mlsanity.com/paper/2606.06985/) (2026)
- [Adding Robust Code-Switching Capabilities to High Performance Multilingual ASR](https://mlsanity.com/paper/2606.21990/) (2026)

## Abstract (arXiv)

Despite achieving impressive results on standard benchmarks, large foundational models still struggle against code-switching test cases. When data scarcity cannot be used as the usual justification for poor performance, the reason may lie in the infrequent occurrence of code-switched moments, where the embedding of the second language appears subtly. Instead of expecting the models to learn this infrequency on their own, it might be beneficial to provide the training process with labels. Evaluating model performance on code-switching data requires careful localization of code-switching points where recognition errors are most consequential, so that the analysis emphasizes mistakes occurring at those moments. Building on this observation, we leverage the difference between the embedded and the main language to highlight those code-switching points and thereby emphasize learning at those locations. This simple yet effective differentiable surrogate mitigates context bias during generation -- the central challenge in code-switching -- thereby improving the model's robustness. Our experiments with Arabic and Chinese-English showed that the models are able to predict the switching places more correctly, reflected by the reduced substitution error.