# Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2309.11327 · HTML: https://mlsanity.com/paper/2309.11327/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [A Survey of Code-switched Speech and Language Processing](https://mlsanity.com/paper/1904.00784/) (2019)
- [The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results](https://mlsanity.com/paper/2007.05916/) (2020)
- [Speech Self-Supervised Representation Benchmarking: Are We Doing it Right?](https://mlsanity.com/paper/2306.00452/) (2023)
- [Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech](https://mlsanity.com/paper/2108.12881/) (2021)
- [Towards Zero-Shot Code-Switched Speech Recognition](https://mlsanity.com/paper/2211.01458/) (2022)
- [Continuous Pseudo-Labeling from the Start](https://mlsanity.com/paper/2210.08711/) (2022)
- [ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks](https://mlsanity.com/paper/2205.01987/) (2022)

## Built on by (most cited first, 7 of 7)

- [PIER: A Novel Metric for Evaluating What Matters in Code-Switching](https://mlsanity.com/paper/2501.09512/) (2025)
- [Weight Factorization and Centralization for Continual Learning in Speech Recognition](https://mlsanity.com/paper/2506.16574/) (2025)
- [The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties](https://mlsanity.com/paper/2509.07139/) (2025)
- [CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Language Bias for Code-Switching Speech Recognition](https://mlsanity.com/paper/2412.12760/) (2024)
- [ADI-20: Arabic Dialect Identification dataset and models](https://mlsanity.com/paper/2511.10070/) (2025)
- [Adapting Language Balance in Code-Switching Speech](https://mlsanity.com/paper/2510.18724/) (2025)
- [Bayesian Low-Rank Factorization for Robust Model Adaptation](https://mlsanity.com/paper/2510.18723/) (2025)

## Abstract (arXiv)

Crafting an effective Automatic Speech Recognition (ASR) solution for dialects demands innovative approaches that not only address the data scarcity issue but also navigate the intricacies of linguistic diversity. In this paper, we address the aforementioned ASR challenge, focusing on the Tunisian dialect. First, textual and audio data is collected and in some cases annotated. Second, we explore self-supervision, semi-supervision and few-shot code-switching approaches to push the state-of-the-art on different Tunisian test sets; covering different acoustic, linguistic and prosodic conditions. Finally, and given the absence of conventional spelling, we produce a human evaluation of our transcripts to avoid the noise coming from spelling inadequacies in our testing references. Our models, allowing to transcribe audio samples in a linguistic mix involving Tunisian Arabic, English and French, and all the data used during training and testing are released for public use and further improvements.