# Adding Robust Code-Switching Capabilities to High Performance Multilingual ASR

arXiv: https://arxiv.org/abs/2606.21990 · HTML: https://mlsanity.com/paper/2606.21990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 22 of 22)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model](https://mlsanity.com/paper/2406.04904/) (2024)
- [DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders](https://mlsanity.com/paper/2106.13736/) (2021)
- [Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR](https://mlsanity.com/paper/2105.14779/) (2021)
- [End-to-End Speech Translation for Code Switched Speech](https://mlsanity.com/paper/2204.05076/) (2022)
- [Data Augmentation for End-to-end Code-switching Speech Recognition](https://mlsanity.com/paper/2011.02160/) (2020)
- [Arabic Code-Switching Speech Recognition using Monolingual Data](https://mlsanity.com/paper/2107.01573/) (2021)
- [Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning](https://mlsanity.com/paper/1805.12070/) (2018)
- [Improving Low Resource Code-switched ASR using Augmented Code-switched TTS](https://mlsanity.com/paper/2010.05549/) (2020)
- [Efficient Weight factorization for Multilingual Speech Recognition](https://mlsanity.com/paper/2105.03010/) (2021)
- [CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset](https://mlsanity.com/paper/2509.14161/) (2025)
- [PIER: A Novel Metric for Evaluating What Matters in Code-Switching](https://mlsanity.com/paper/2501.09512/) (2025)
- [Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition](https://mlsanity.com/paper/2210.08992/) (2022)
- [Reducing language context confusion for end-to-end code-switching automatic speech recognition](https://mlsanity.com/paper/2201.12155/) (2022)
- [Weight Factorization and Centralization for Continual Learning in Speech Recognition](https://mlsanity.com/paper/2506.16574/) (2025)
- [An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR](https://mlsanity.com/paper/2503.08954/) (2025)
- [Can we train ASR systems on Code-switch without real code-switch data? Case study for Singapore's languages](https://mlsanity.com/paper/2506.14177/) (2025)
- [Adapting Whisper for Parameter-efficient Code-Switching Speech Recognition via Soft Prompt Tuning](https://mlsanity.com/paper/2506.21576/) (2025)
- [Adapting Language Balance in Code-Switching Speech](https://mlsanity.com/paper/2510.18724/) (2025)
- [Bayesian Low-Rank Factorization for Robust Model Adaptation](https://mlsanity.com/paper/2510.18723/) (2025)

## Abstract (arXiv)

Code-switching (CSW) remains challenging for large multi-lingual ASR systems in real-world deployment. While fine-tuning on synthetic CSW data is possible, it generally degrades strong monolingual baselines. Our goal is to preserve these capabilities while extending models to handle complex code-switching, including morphological variations across languages. We propose Bayesian factorized adaptation, which learns to efficiently integrate switching-relevant knowledge into strong pretrained models without overwriting existing capabilities. Requiring only a small amount of synthetic data, our approach reduces transcription errors by 32.87% on code-switched words while improving overall WER by 5.31%, all while maintaining mono-lingual performance. Our results demonstrate that effective CSW adaptation depends more on knowledge integration than data complexity.