# Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR

arXiv: https://arxiv.org/abs/2105.14779 · HTML: https://mlsanity.com/paper/2105.14779/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 31 May 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters](https://mlsanity.com/paper/2007.03001/) (2020)
- [Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model](https://mlsanity.com/paper/1909.05330/) (2019)
- [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)
- [Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytes](https://mlsanity.com/paper/1811.09021/) (2018)
- [Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling](https://mlsanity.com/paper/1810.03459/) (2018)
- [Multi-Encoder-Decoder Transformer for Code-Switching Speech Recognition](https://mlsanity.com/paper/2006.10414/) (2020)
- [Language-agnostic Multilingual Modeling](https://mlsanity.com/paper/2004.09571/) (2020)
- [Arabic Speech Recognition by End-to-End, Modular Systems and Human](https://mlsanity.com/paper/2101.08454/) (2021)

## Built on by (most cited first, 30 of 41)

- [ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation](https://mlsanity.com/paper/2112.06223/) (2021)
- [QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus](https://mlsanity.com/paper/2106.13000/) (2021)
- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [Can Whisper perform speech-based in-context learning?](https://mlsanity.com/paper/2309.07081/) (2023)
- [N-Shot Benchmarking of Whisper on Diverse Arabic Speech Recognition](https://mlsanity.com/paper/2306.02902/) (2023)
- [Arabic Code-Switching Speech Recognition using Monolingual Data](https://mlsanity.com/paper/2107.01573/) (2021)
- [LAraBench: Benchmarking Arabic AI with Large Language Models](https://mlsanity.com/paper/2305.14982/) (2023)
- [Representativeness as a Forgotten Lesson for Multilingual and Code-switched Data Collection and Preparation](https://mlsanity.com/paper/2310.20470/) (2023)
- [ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English](https://mlsanity.com/paper/2211.12000/) (2022)
- [CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset](https://mlsanity.com/paper/2509.14161/) (2025)
- [Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition](https://mlsanity.com/paper/2201.02550/) (2022)
- [Speech collage: code-switched audio generation by collaging monolingual corpora](https://mlsanity.com/paper/2309.15674/) (2023)
- [ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus](https://mlsanity.com/paper/2403.18182/) (2024)
- [What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis](https://mlsanity.com/paper/2107.00439/) (2021)
- [Automatic Pronunciation Assessment -- A Review](https://mlsanity.com/paper/2310.13974/) (2023)
- [Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition](https://mlsanity.com/paper/2211.16319/) (2022)
- [Multilingual Word Error Rate Estimation: e-WER3](https://mlsanity.com/paper/2304.00649/) (2023)
- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (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)
- [Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition](https://mlsanity.com/paper/2311.03196/) (2023)
- [To Distill or Not to Distill? On the Robustness of Robust Knowledge Distillation](https://mlsanity.com/paper/2406.04512/) (2024)
- [Open Universal Arabic ASR Leaderboard](https://mlsanity.com/paper/2412.13788/) (2024)
- [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025)
- [VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System](https://mlsanity.com/paper/2310.11069/) (2023)
- [Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study](https://mlsanity.com/paper/2310.15262/) (2023)
- [TEDxTN: A Three-way Speech Translation Corpus for Code-Switched Tunisian Arabic - English](https://mlsanity.com/paper/2511.10780/) (2025)
- [Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs](https://mlsanity.com/paper/2601.12494/) (2026)
- [Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning](https://mlsanity.com/paper/2305.19759/) (2023)
- [Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices](https://mlsanity.com/paper/2509.02523/) (2025)
- [Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition](https://mlsanity.com/paper/2106.05885/) (2021)

## Abstract (arXiv)

With the advent of globalization, there is an increasing demand for multilingual automatic speech recognition (ASR), handling language and dialectal variation of spoken content. Recent studies show its efficacy over monolingual systems. In this study, we design a large multilingual end-to-end ASR using self-attention based conformer architecture. We trained the system using Arabic (Ar), English (En) and French (Fr) languages. We evaluate the system performance handling: (i) monolingual (Ar, En and Fr); (ii) multi-dialectal (Modern Standard Arabic, along with dialectal variation such as Egyptian and Moroccan); (iii) code-switching -- cross-lingual (Ar-En/Fr) and dialectal (MSA-Egyptian dialect) test cases, and compare with current state-of-the-art systems. Furthermore, we investigate the influence of different embedding/character representations including character vs word-piece; shared vs distinct input symbol per language. Our findings demonstrate the strength of such a model by outperforming state-of-the-art monolingual dialectal Arabic and code-switching Arabic ASR.