# Data Augmentation for End-to-end Code-switching Speech Recognition

arXiv: https://arxiv.org/abs/2011.02160 · HTML: https://mlsanity.com/paper/2011.02160/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 20 of 20)

- [QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus](https://mlsanity.com/paper/2106.13000/) (2021)
- [End-to-End Speech Translation for Code Switched Speech](https://mlsanity.com/paper/2204.05076/) (2022)
- [On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR](https://mlsanity.com/paper/2104.01393/) (2021)
- [Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models](https://mlsanity.com/paper/2110.03504/) (2021)
- [Non-autoregressive Mandarin-English Code-switching Speech Recognition](https://mlsanity.com/paper/2104.02258/) (2021)
- [Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition](https://mlsanity.com/paper/2210.08992/) (2022)
- [LAE-ST-MoE: Boosted Language-Aware Encoder Using Speech Translation Auxiliary Task for E2E Code-switching ASR](https://mlsanity.com/paper/2309.16178/) (2023)
- [SC-MoE: Switch Conformer Mixture of Experts for Unified Streaming and Non-streaming Code-Switching ASR](https://mlsanity.com/paper/2406.18021/) (2024)
- [Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition](https://mlsanity.com/paper/2207.04176/) (2022)
- [Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization](https://mlsanity.com/paper/2302.05110/) (2023)
- [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025)
- [CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Language Bias for Code-Switching Speech Recognition](https://mlsanity.com/paper/2412.12760/) (2024)
- [Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation](https://mlsanity.com/paper/2306.08588/) (2023)
- [AsyncSwitch: Asynchronous Text-Speech Adaptation for Code-Switched ASR](https://mlsanity.com/paper/2506.14190/) (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)
- [Adapting Language Balance in Code-Switching Speech](https://mlsanity.com/paper/2510.18724/) (2025)
- [Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms](https://mlsanity.com/paper/2105.05791/) (2021)
- [Bayesian Low-Rank Factorization for Robust Model Adaptation](https://mlsanity.com/paper/2510.18723/) (2025)
- [Enhancing Code-switched Text-to-Speech Synthesis Capability in Large Language Models with only Monolingual Corpora](https://mlsanity.com/paper/2409.10969/) (2024)
- [Adding Robust Code-Switching Capabilities to High Performance Multilingual ASR](https://mlsanity.com/paper/2606.21990/) (2026)

## Abstract (arXiv)

Training a code-switching end-to-end automatic speech recognition (ASR) model normally requires a large amount of data, while code-switching data is often limited. In this paper, three novel approaches are proposed for code-switching data augmentation. Specifically, they are audio splicing with the existing code-switching data, and TTS with new code-switching texts generated by word translation or word insertion. Our experiments on 200 hours Mandarin-English code-switching dataset show that all the three proposed approaches yield significant improvements on code-switching ASR individually. Moreover, all the proposed approaches can be combined with recent popular SpecAugment, and an addition gain can be obtained. WER is significantly reduced by relative 24.0% compared to the system without any data augmentation, and still relative 13.0% gain compared to the system with only SpecAugment