# AfriBED: a Speech Recognition Bias Evaluation Dataset for Low Resource African Languages

arXiv: https://arxiv.org/abs/2610.26821 · HTML: https://mlsanity.com/paper/2610.26821/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in ASR evaluation, bias and atypical speech, one of 11 directions in Speech and audio language models.
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 19 Oct 2026.

## Placed in

- Speech and audio language models / [ASR evaluation, bias and atypical speech](https://mlsanity.com/speech/asr-evaluation-bias-and-atypical-speech/)

## Builds on (most cited first, 10 of 10)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Towards measuring fairness in speech recognition: Fair-Speech dataset](https://mlsanity.com/paper/2408.12734/) (2024)
- [Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions](https://mlsanity.com/paper/2111.09983/) (2021)
- [Zambezi Voice: A Multilingual Speech Corpus for Zambian Languages](https://mlsanity.com/paper/2306.04428/) (2023)
- [BembaSpeech: A Speech Recognition Corpus for the Bemba Language](https://mlsanity.com/paper/2102.04889/) (2021)
- [BIG-C: a Multimodal Multi-Purpose Dataset for Bemba](https://mlsanity.com/paper/2305.17202/) (2023)
- [Africa-Centric Self-Supervised Pre-Training for Multilingual Speech Representation in a Sub-Saharan Context](https://mlsanity.com/paper/2404.02000/) (2024)
- [Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions](https://mlsanity.com/paper/2505.11690/) (2025)

## Abstract (arXiv)

Despite advancements in automatic speech recognition systems (ASR), current systems exhibit speaker-attribute bias, i.e., their speech recognition performance is biased against certain speakers based on their demographic attributes such as gender, accents, and speaker type. However, accurately assessing speaker attribute bias in a given ASR system requires the availability of a bias evaluation dataset with comparable speech across subgroups of a speaker attribute. In this paper, we introduce African speech Bias Evaluation Dataset (AfriBED), a novel speech recognition bias evaluation dataset for two low resource African languages, Bemba and Nyanja, to detect and estimate gender and speaker-type bias for ASR systems. The dataset comprises 9 hours of speech with 3928 utterances recorded by 80 speakers for Bemba, while the Nyanja set includes 6260 utterances with approximately 18 hours of speech recorded by 127 speakers. The results of our baseline experiments demonstrate that the datasets are useful for examining gender and speaker-type attribute bias in the target languages.