# Where Are We At with Automatic Speech Recognition for the Bambara Language?

arXiv: https://arxiv.org/abs/2602.09785 · HTML: https://mlsanity.com/paper/2602.09785/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning](https://mlsanity.com/paper/2506.17525/) (2025)
- [The State of Large Language Models for African Languages: Progress and Challenges](https://mlsanity.com/paper/2506.02280/) (2025)
- [Benchmarking Automatic Speech Recognition Models for African Languages](https://mlsanity.com/paper/2512.10968/) (2025)

## Built on by (most cited first, 1 of 1)

- [Building an ASR Solution for Training and Assessing Children's Reading](https://mlsanity.com/paper/2606.31508/) (2026)

## Abstract (arXiv)

This paper introduces the first standardized benchmark for evaluating Automatic Speech Recognition (ASR) in the Bambara language, utilizing one hour of professionally recorded Malian constitutional text. Designed as a controlled reference set under near-optimal acoustic and linguistic conditions, the benchmark was used to evaluate 37 models, ranging from Bambara-trained systems to large-scale commercial models. Our findings reveal that current ASR performance remains significantly below deployment standards in a narrow formal domain; the top-performing system in terms of Word Error Rate (WER) achieved 46.76% and the best Character Error Rate (CER) of 13.00% was set by another model, while several prominent multilingual models exceeded 100% WER. These results suggest that multilingual pre-training and model scaling alone are insufficient for underrepresented languages. Furthermore, because this dataset represents a best-case scenario of the most simplified and formal form of spoken Bambara, these figures are yet to be tested against practical, real-world settings. We provide the benchmark and an accompanying public leaderboard to facilitate transparent evaluation and future research in Bambara speech technology.