# End-to-End Code-Switching ASR for Low-Resourced Language Pairs

arXiv: https://arxiv.org/abs/1909.12681 · HTML: https://mlsanity.com/paper/1909.12681/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 15 of 15)

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [End-to-End Speech Translation for Code Switched Speech](https://mlsanity.com/paper/2204.05076/) (2022)
- [Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech](https://mlsanity.com/paper/2108.12881/) (2021)
- [SwitchLingua: The First Large-Scale Multilingual and Multi-Ethnic Code-Switching Dataset](https://mlsanity.com/paper/2506.00087/) (2025)
- [Generative error correction for code-switching speech recognition using large language models](https://mlsanity.com/paper/2310.13013/) (2023)
- [LAE-ST-MoE: Boosted Language-Aware Encoder Using Speech Translation Auxiliary Task for E2E Code-switching ASR](https://mlsanity.com/paper/2309.16178/) (2023)
- [Adapting the adapters for code-switching in multilingual ASR](https://mlsanity.com/paper/2310.07423/) (2023)
- [Speaker Extraction with Co-Speech Gestures Cue](https://mlsanity.com/paper/2203.16840/) (2022)
- [Towards Real-World Streaming Speech Translation for Code-Switched Speech](https://mlsanity.com/paper/2310.12648/) (2023)
- [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025)
- [Predicting User Code-Switching Level from Sociological and Psychological Profiles](https://mlsanity.com/paper/2112.06462/) (2021)
- [Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation](https://mlsanity.com/paper/2306.08588/) (2023)
- [The Who in Code-Switching: A Case Study for Predicting Egyptian Arabic-English Code-Switching Levels based on Character Profiles](https://mlsanity.com/paper/2208.00433/) (2022)
- [Romanization Encoding For Multilingual ASR](https://mlsanity.com/paper/2407.04368/) (2024)
- [Speech Separation with Pretrained Frontend to Minimize Domain Mismatch](https://mlsanity.com/paper/2411.03085/) (2024)

## Abstract (arXiv)

Despite the significant progress in end-to-end (E2E) automatic speech recognition (ASR), E2E ASR for low resourced code-switching (CS) speech has not been well studied. In this work, we describe an E2E ASR pipeline for the recognition of CS speech in which a low-resourced language is mixed with a high resourced language. Low-resourcedness in acoustic data hinders the performance of E2E ASR systems more severely than the conventional ASR systems.~To mitigate this problem in the transcription of archives with code-switching Frisian-Dutch speech, we integrate a designated decoding scheme and perform rescoring with neural network-based language models to enable better utilization of the available textual resources. We first incorporate a multi-graph decoding approach which creates parallel search spaces for each monolingual and mixed recognition tasks to maximize the utilization of the textual resources from each language. Further, language model rescoring is performed using a recurrent neural network pre-trained with cross-lingual embedding and further adapted with the limited amount of in-domain CS text. The ASR experiments demonstrate the effectiveness of the described techniques in improving the recognition performance of an E2E CS ASR system in a low-resourced scenario.