跨语言语音模型安全与公平性存漏洞，真实语料测试发现非英语下问题更严重
RedVox: Safety and Fairness Gaps in Speech Models Across Languages
- 基于真实语音构建多语言安全评测基准RedVox
- 8个主流模型在非英语语境下安全漏洞加剧，口语输入时更明显
- 揭示语音数据采集中的隐私与伦理挑战，适合关注AI公平性的研究者
语音模型在多语言实际应用中日益普及，但其在英语以外的语言环境及自然场景下的安全与公平性仍缺乏研究。我们调研了前沿语音模型发布中的安全报告实践，发现仅有8%的模型进行了多语言分析。为填补这一空白，我们提出RedVox——一个基于真实语音、覆盖英、法、意、西、德五种语言的多语言语音安全与公平性基准，评估对不当和刻板化请求的响应。对8个先进模型的评估显示，即使在非对抗性条件下，漏洞依然存在，且在非英语语言中恶化，在语音输入时进一步放大。通过调查红队参与者，我们记录了真实语音数据收集中的独特个人与隐私挑战，揭示了自然主义语音安全研究背后的深层社会技术难题。
原文摘要 · Abstract (English)
Speech-capable models are increasingly deployed in real-world applications across languages. Yet their safety and fairness beyond English settings and under naturalistic conditions remain understudied. We survey safety reporting practices across state-of-the-art speech model releases, finding that only 8% document any multilingual analysis. To address this gap, we introduce RedVox, a multilingual safety and fairness benchmark for audio and speech built on real voices, covering unsafe and unfair stereotypical requests across five languages (English, French, Italian, Spanish, and German). Evaluating eight state-of-the-art models, we find that vulnerabilities persist even under non-adversarial conditions, worsen in non-English languages, and are amplified when the request comes from a spoken input. Finally, by surveying the participants who contributed to RedVox, we document the unique personal and privacy challenges of collecting speech data with human participants, pointing to broader sociotechnical challenges in naturalistic speech safety research.
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