# Meta-Transfer Learning for Code-Switched Speech Recognition

arXiv: https://arxiv.org/abs/2004.14228 · HTML: https://mlsanity.com/paper/2004.14228/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 22 of 22)

- [Multilingual Large Language Models Are Not (Yet) Code-Switchers](https://mlsanity.com/paper/2305.14235/) (2023)
- [Are Multilingual Models Effective in Code-Switching?](https://mlsanity.com/paper/2103.13309/) (2021)
- [ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation](https://mlsanity.com/paper/2112.06223/) (2021)
- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [A Configurable Multilingual Model is All You Need to Recognize All Languages](https://mlsanity.com/paper/2107.05876/) (2021)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset](https://mlsanity.com/paper/2201.02419/) (2022)
- [Model Generalization on COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03841/) (2021)
- [X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering](https://mlsanity.com/paper/2104.09696/) (2021)
- [CI-AVSR: A Cantonese Audio-Visual Speech Dataset for In-car Command Recognition](https://mlsanity.com/paper/2201.03804/) (2022)
- [Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning](https://mlsanity.com/paper/2306.15686/) (2023)
- [Meta Transfer Learning for Early Success Prediction in MOOCs](https://mlsanity.com/paper/2205.01064/) (2022)
- [Greenformers: Improving Computation and Memory Efficiency in Transformer Models via Low-Rank Approximation](https://mlsanity.com/paper/2108.10808/) (2021)
- [IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages](https://mlsanity.com/paper/2311.12405/) (2023)
- [Rapid Language Adaptation for Multilingual E2E Speech Recognition Using Encoder Prompting](https://mlsanity.com/paper/2406.12611/) (2024)
- [Monolingual Recognizers Fusion for Code-switching Speech Recognition](https://mlsanity.com/paper/2211.01046/) (2022)
- [AsyncSwitch: Asynchronous Text-Speech Adaptation for Code-Switched ASR](https://mlsanity.com/paper/2506.14190/) (2025)
- [Multilingual Sentence-Level Semantic Search using Meta-Distillation Learning](https://mlsanity.com/paper/2309.08185/) (2023)
- [Towards Fine-Grained Code-Switch Speech Translation with Semantic Space Alignment](https://mlsanity.com/paper/2511.10670/) (2025)
- [Adaptive Activation Network For Low Resource Multilingual Speech Recognition](https://mlsanity.com/paper/2205.14326/) (2022)
- [Meta Auxiliary Learning for Low-resource Spoken Language Understanding](https://mlsanity.com/paper/2206.12774/) (2022)
- [Polyglot-Lion: Efficient Multilingual ASR for Singapore via Balanced Fine-Tuning of Qwen3-ASR](https://mlsanity.com/paper/2603.16184/) (2026)

## Abstract (arXiv)

An increasing number of people in the world today speak a mixed-language as a result of being multilingual. However, building a speech recognition system for code-switching remains difficult due to the availability of limited resources and the expense and significant effort required to collect mixed-language data. We therefore propose a new learning method, meta-transfer learning, to transfer learn on a code-switched speech recognition system in a low-resource setting by judiciously extracting information from high-resource monolingual datasets. Our model learns to recognize individual languages, and transfer them so as to better recognize mixed-language speech by conditioning the optimization on the code-switching data. Based on experimental results, our model outperforms existing baselines on speech recognition and language modeling tasks, and is faster to converge.