VoxSafeBench: Not Just What Is Said, but Who, How, and Where
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| Format: | Preprint |
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2026
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| _version_ | 1866911605408661504 |
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| author | Wang, Yuxiang Liu, Hongyu Xu, Yijiang Ni, Qinke Wang, Li Lin, Wan Feng, Kunyu Chen, Dekun Tan, Xu Wang, Lei Shi, Jie Wu, Zhizheng |
| author_facet | Wang, Yuxiang Liu, Hongyu Xu, Yijiang Ni, Qinke Wang, Li Lin, Wan Feng, Kunyu Chen, Dekun Tan, Xu Wang, Lei Shi, Jie Wu, Zhizheng |
| contents | As speech language models (SLMs) transition from personal devices into shared, multi-user environments, their responses must account for far more than the words alone. Who is speaking, how they sound, and where the conversation takes place can each turn an otherwise benign request into one that is unsafe, unfair, or privacy-violating. Existing benchmarks, however, largely focus on basic audio comprehension, study individual risks in isolation, or conflate content that is inherently harmful with content that only becomes problematic due to its acoustic context. We introduce VoxSafeBench, among the first benchmarks to jointly evaluate social alignment in SLMs across three dimensions: safety, fairness, and privacy. VoxSafeBench adopts a Two-Tier design: Tier1 evaluates content-centric risks using matched text and audio inputs, while Tier2 targets audio-conditioned risks in which the transcript is benign but the appropriate response hinges on the speaker, paralinguistic cues, or the surrounding environment. To validate Tier2, we include intermediate perception probes and confirm that frontier SLMs can successfully detect these acoustic cues yet still fail to act on them appropriately. Across 22 tasks with bilingual coverage, we find that safeguards appearing robust on text often degrade in speech: safety awareness drops for speaker- and scene-conditioned risks, fairness erodes when demographic differences are conveyed vocally, and privacy protections falter when contextual cues arrive acoustically. Together, these results expose a pervasive speech grounding gap: current SLMs frequently recognize the relevant social norm in text but fail to apply it when the decisive cue must be grounded in speech. Code and data are publicly available at: https://amphionteam.github.io/VoxSafeBench_demopage/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14548 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VoxSafeBench: Not Just What Is Said, but Who, How, and Where Wang, Yuxiang Liu, Hongyu Xu, Yijiang Ni, Qinke Wang, Li Lin, Wan Feng, Kunyu Chen, Dekun Tan, Xu Wang, Lei Shi, Jie Wu, Zhizheng Sound Machine Learning Audio and Speech Processing As speech language models (SLMs) transition from personal devices into shared, multi-user environments, their responses must account for far more than the words alone. Who is speaking, how they sound, and where the conversation takes place can each turn an otherwise benign request into one that is unsafe, unfair, or privacy-violating. Existing benchmarks, however, largely focus on basic audio comprehension, study individual risks in isolation, or conflate content that is inherently harmful with content that only becomes problematic due to its acoustic context. We introduce VoxSafeBench, among the first benchmarks to jointly evaluate social alignment in SLMs across three dimensions: safety, fairness, and privacy. VoxSafeBench adopts a Two-Tier design: Tier1 evaluates content-centric risks using matched text and audio inputs, while Tier2 targets audio-conditioned risks in which the transcript is benign but the appropriate response hinges on the speaker, paralinguistic cues, or the surrounding environment. To validate Tier2, we include intermediate perception probes and confirm that frontier SLMs can successfully detect these acoustic cues yet still fail to act on them appropriately. Across 22 tasks with bilingual coverage, we find that safeguards appearing robust on text often degrade in speech: safety awareness drops for speaker- and scene-conditioned risks, fairness erodes when demographic differences are conveyed vocally, and privacy protections falter when contextual cues arrive acoustically. Together, these results expose a pervasive speech grounding gap: current SLMs frequently recognize the relevant social norm in text but fail to apply it when the decisive cue must be grounded in speech. Code and data are publicly available at: https://amphionteam.github.io/VoxSafeBench_demopage/ |
| title | VoxSafeBench: Not Just What Is Said, but Who, How, and Where |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2604.14548 |