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

Paper recorded by Signals 4 on 2026-10-19 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-19 on arXiv · recorded by Signals 4 on 2026-10-20

Category: cs.AI · 人工智能 · first seen 2026-10-20

Abstract

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 dat

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#254 most recent of 960 cs.AI papers we have recorded · ↑ newer: RefCraft: Benchmarking Visual-Reference-Conditioned Agentic Generation · ↓ older: Next Best Action: Causal Constrained Offline RL for Business Action Ta
Cite this page: AfriBED: a Speech Recognition Bias Evaluation Dataset for Low Resource African Languages: the #254 most recent of 960 cs.AI papers we have recorded (as of 2026-10-19). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/afribed-a-speech-recognition-bias-evaluation-dataset-for-low-resource-african-la.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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