一项新的研究论文强调了自动语音识别（ASR）系统中偏差所带来的情感和心理负担。该研究在美国四个地点进行了用户体验研究，发现参与者常常觉得技术未能顾及他们的文化背景。这导致了自卑感和沮丧感，因为用户付出了大量的隐性劳动，例如语码转换和过度清晰发音，才能使系统正常工作。该论文认为，传统的算法公平性准确性指标忽略了这些关键的伤害维度。 AI
影响 强调了ASR评估超越准确性指标的必要性，需包含用户体验和情感影响。
排序理由 关于ASR系统偏差的学术论文。
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arXiv:2604.21148v2 Announce Type: replace Abstract: Studies on bias in Automatic Speech Recognition (ASR) tend to focus on reporting error rates for speakers of underrepresented dialects, yet less research examines the human side of system bias: how do system failures shape users…
Studies on bias in Automatic Speech Recognition (ASR) tend to focus on reporting error rates for speakers of underrepresented dialects, yet less research examines the human side of system bias: how do system failures shape users' lived experiences, how do users feel about and rea…