# Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech

arXiv: https://arxiv.org/abs/2108.12881 · HTML: https://mlsanity.com/paper/2108.12881/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 118 references that are in the map.
- First posted on arXiv 29 Aug 2021.

## Builds on (most cited first, 15 of 15)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [Multilingual Speech Recognition With A Single End-To-End Model](https://mlsanity.com/paper/1711.01694/) (2017)
- [Speech Recognition Challenge in the Wild: Arabic MGB-3](https://mlsanity.com/paper/1709.07276/) (2017)
- [Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM](https://mlsanity.com/paper/1706.02737/) (2017)
- [On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition](https://mlsanity.com/paper/1811.00241/) (2018)
- [Challenges of Computational Processing of Code-Switching](https://mlsanity.com/paper/1610.02213/) (2016)
- [Towards End-to-End Code-Switching Speech Recognition](https://mlsanity.com/paper/1810.13091/) (2018)
- [End-to-End Code-Switching ASR for Low-Resourced Language Pairs](https://mlsanity.com/paper/1909.12681/) (2019)
- [Building a Unified Code-Switching ASR System for South African Languages](https://mlsanity.com/paper/1807.10949/) (2018)
- [Semi-supervised acoustic model training for five-lingual code-switched ASR](https://mlsanity.com/paper/1906.08647/) (2019)
- [End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning](https://mlsanity.com/paper/1908.04737/) (2019)
- [Code-switching Language Modeling With Bilingual Word Embeddings: A Case Study for Egyptian Arabic-English](https://mlsanity.com/paper/1909.10892/) (2019)
- [Towards End-to-end Automatic Code-Switching Speech Recognition](https://mlsanity.com/paper/1810.12620/) (2018)

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

- [ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English](https://mlsanity.com/paper/2211.12000/) (2022)
- [Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition](https://mlsanity.com/paper/2201.02550/) (2022)
- [ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus](https://mlsanity.com/paper/2403.18182/) (2024)
- [Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation](https://mlsanity.com/paper/2205.12649/) (2022)
- [Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text](https://mlsanity.com/paper/2210.06990/) (2022)
- [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)
- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)
- [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025)
- [Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study](https://mlsanity.com/paper/2310.15262/) (2023)
- [Predicting User Code-Switching Level from Sociological and Psychological Profiles](https://mlsanity.com/paper/2112.06462/) (2021)
- [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)
- [Bulbul: A Dataset for Dialectal Arabic Speech Recognition](https://mlsanity.com/paper/2608.21950/) (2026)
- [AI-based Arabic Language and Speech Tutor](https://mlsanity.com/paper/2210.12346/) (2022)
- [MUSCAT: MUltilingual, SCientific ConversATion Benchmark](https://mlsanity.com/paper/2604.15929/) (2026)

## Abstract (arXiv)

Code-switching (CS), defined as the mixing of languages in conversations, has become a worldwide phenomenon. The prevalence of CS has been recently met with a growing demand and interest to build CS ASR systems. In this paper, we present our work on code-switched Egyptian Arabic-English automatic speech recognition (ASR). We first contribute in filling the huge gap in resources by collecting, analyzing and publishing our spontaneous CS Egyptian Arabic-English speech corpus. We build our ASR systems using DNN-based hybrid and Transformer-based end-to-end models. In this paper, we present a thorough comparison between both approaches under the setting of a low-resource, orthographically unstandardized, and morphologically rich language pair. We show that while both systems give comparable overall recognition results, each system provides complementary sets of strength points. We show that recognition can be improved by combining the outputs of both systems. We propose several effective system combination approaches, where hypotheses of both systems are merged on sentence- and word-levels. Our approaches result in overall WER relative improvement of 4.7%, over a baseline performance of 32.1% WER. In the case of intra-sentential CS sentences, we achieve WER relative improvement of 4.8%. Our best performing system achieves 30.6% WER on ArzEn test set.