RedVox：语音模型中跨语言的安全与公平差距

摘要

具备多语言能力的语音模型正越来越多地部署于现实世界的各类应用中。然而，这些模型在英语环境之外及自然情境下的安全性与公平性仍研究不足。我们调查了当前最先进语音模型发布中的安全报告实践，发现仅8%的报告记录了多语言分析。为弥补这一空白，我们提出了RedVox——一个基于真实语音构建的多语言音频与语音安全及公平性基准，涵盖五种语言（英语、法语、意大利语、西班牙语和德语）中的不安全及不公平刻板请求。通过对八款最先进模型的评估，我们发现：即使在非对抗性条件下，漏洞依然存在；在非英语语言中问题更为严重；且当请求通过语音输入提出时，风险被进一步放大。最后，通过调查参与RedVox数据收集的贡献者，我们记录了采集人类参与者语音数据所面临的独特个人隐私挑战，揭示了自然语音安全研究中更广泛的社会技术难题。

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.