AfriBED: a Speech Recognition Bias Evaluation Dataset for Low Resource African Languages
Testing whether speech recognition systems treat African language speakers fairly
Speech recognition systems work worse for some speakers than others based on gender and accent, but testing for this bias in African languages has been nearly impossible due to lack of data. Researchers created AfriBED, a dataset of 9 to 18 hours of recorded speech in Bemba and Nyanja from 80 to 127 speakers, specifically designed to measure gender and speaker-type bias in these low-resource languages.
As speech technology spreads to African languages and regions, biased systems could systematically exclude women or certain speaker types from accessing voice-controlled services, information, and opportunities. This dataset gives researchers and companies a concrete way to detect and fix these problems before deployment, ensuring technology works equally well regardless of who's speaking.