{
  "id": 15702437,
  "title": "AfriBED: a Speech Recognition Bias Evaluation Dataset for Low Resource African Languages",
  "url": "https://urgent.news/2026/10/19/afribed-a-speech-recognition-bias-evaluation-dataset-for-low-resource",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-19T17:53:32.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.26821v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}