# Voices Unheard: NLP Resources and Models for Yor`ub'a Regional Dialects

arXiv: https://arxiv.org/abs/2406.19564 · HTML: https://mlsanity.com/paper/2406.19564/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 80 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 30 of 35)

- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [What do Neural Machine Translation Models Learn about Morphology?](https://mlsanity.com/paper/1704.03471/) (2017)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- [Multi-VALUE: A Framework for Cross-Dialectal English NLP](https://mlsanity.com/paper/2212.08011/) (2022)
- [DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages](https://mlsanity.com/paper/2403.11009/) (2024)
- [The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation](https://mlsanity.com/paper/2110.03036/) (2021)
- [Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go](https://mlsanity.com/paper/2203.08351/) (2022)
- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translation](https://mlsanity.com/paper/2103.08647/) (2021)
- [Quantifying the Dialect Gap and its Correlates Across Languages](https://mlsanity.com/paper/2310.15135/) (2023)
- [NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2310.16117/) (2023)
- [A Survey of Corpora for Germanic Low-Resource Languages and Dialects](https://mlsanity.com/paper/2304.09805/) (2023)
- [BembaSpeech: A Speech Recognition Corpus for the Bemba Language](https://mlsanity.com/paper/2102.04889/) (2021)
- [NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification](https://mlsanity.com/paper/2305.10971/) (2023)
- [CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation](https://mlsanity.com/paper/2305.17267/) (2023)
- [varepsilon K'U <MASK>: Integrating Yor`ub'a cultural greetings into machine translation](https://mlsanity.com/paper/2303.17972/) (2023)
- [`Ir`oy`inSpeech: A multi-purpose Yor`ub'a Speech Corpus](https://mlsanity.com/paper/2307.16071/) (2023)
- [Very Low Resource Sentence Alignment: Luhya and Swahili](https://mlsanity.com/paper/2211.00046/) (2022)
- [Designing for Harm Reduction: Communication Repair for Multicultural Users' Voice Interactions](https://mlsanity.com/paper/2403.00265/) (2024)

## Built on by (most cited first, 11 of 11)

- [AfriHuBERT: A self-supervised speech representation model for African languages](https://mlsanity.com/paper/2409.20201/) (2024)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages](https://mlsanity.com/paper/2505.20564/) (2025)
- [A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation](https://mlsanity.com/paper/2506.02894/) (2025)
- [Bridging Gaps in Natural Language Processing for Yor`ub'a: A Systematic Review of a Decade of Progress and Prospects](https://mlsanity.com/paper/2502.17364/) (2025)
- [Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects](https://mlsanity.com/paper/2510.07890/) (2025)
- [Steering LLMs toward Korean Local Speech: Iterative Refinement Framework for Faithful Dialect Translation](https://mlsanity.com/paper/2511.06680/) (2025)
- [Y-NQ: English-Yor`ub'a Evaluation dataset for Open-Book Reading Comprehension and Text Generation](https://mlsanity.com/paper/2412.08279/) (2024)
- [Speech to Speech Translation with Translatotron: A State of the Art Review](https://mlsanity.com/paper/2502.05980/) (2025)
- [FormosanBench: Benchmarking Low-Resource Austronesian Languages in the Era of Large Language Models](https://mlsanity.com/paper/2506.21563/) (2025)
- [Saar-Voice: A Multi-Speaker Saarbr"ucken Dialect Speech Corpus](https://mlsanity.com/paper/2604.11803/) (2026)

## Abstract (arXiv)

Yor`ub'a an African language with roughly 47 million speakers encompasses a continuum with several dialects. Recent efforts to develop NLP technologies for African languages have focused on their standard dialects, resulting in disparities for dialects and varieties for which there are little to no resources or tools. We take steps towards bridging this gap by introducing a new high-quality parallel text and speech corpus YOR`ULECT across three domains and four regional Yor`ub'a dialects. To develop this corpus, we engaged native speakers, travelling to communities where these dialects are spoken, to collect text and speech data. Using our newly created corpus, we conducted extensive experiments on (text) machine translation, automatic speech recognition, and speech-to-text translation. Our results reveal substantial performance disparities between standard Yor`ub'a and the other dialects across all tasks. However, we also show that with dialect-adaptive finetuning, we are able to narrow this gap. We believe our dataset and experimental analysis will contribute greatly to developing NLP tools for Yor`ub'a and its dialects, and potentially for other African languages, by improving our understanding of existing challenges and offering a high-quality dataset for further development. We release YOR`ULECT dataset and models publicly under an open license.