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arxiv: 2606.26968 · v1 · pith:LUF52NINnew · submitted 2026-06-25 · 💻 cs.CL

RedVox: Safety and Fairness Gaps in Speech Models Across Languages

Beatrice Savoldi , Sara Papi , Wafa Aissa , Matteo Negri , Luisa Bentivogli This is my paper

Pith reviewed 2026-06-26 04:34 UTC · model grok-4.3

classification 💻 cs.CL
keywords speech modelssafety evaluationfairnessmultilingual benchmarkaudio inputnon-English languagesrefusal behaviorstereotypical content
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The pith

Speech models show larger safety and fairness failures in non-English languages and with spoken inputs than with text.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper creates RedVox, a benchmark of real-voice recordings that test speech models on unsafe and unfair stereotypical requests in five languages. It first checks how existing model papers report safety work and finds almost none cover languages other than English. When eight current models are run on the new benchmark, failures appear even without special attack prompts, rise sharply outside English, and rise further when the same request arrives as audio instead of text. The authors also record the extra privacy and consent hurdles that come with gathering spoken data from people. These patterns matter because speech models are moving into everyday use across many countries, where the same safety shortfalls could affect more users.

Core claim

RedVox is a multilingual safety and fairness benchmark for audio and speech built on real voices that covers unsafe and unfair stereotypical requests across English, French, Italian, Spanish, and German. Evaluation of eight state-of-the-art models shows vulnerabilities persist even under non-adversarial conditions, worsen in non-English languages, and are amplified when the request comes from a spoken input. A participant survey further documents the personal and privacy challenges of collecting speech data with human contributors.

What carries the argument

The RedVox benchmark, consisting of real-voice recordings of unsafe and unfair requests in five languages used to measure model refusal rates and bias under spoken versus text conditions.

If this is right

  • Safety testing for speech models must include non-English languages as a standard requirement rather than an optional add-on.
  • Spoken input should be treated as a higher-risk category in safety evaluations because it increases failure rates.
  • Model releases need to document multilingual safety performance, since current practice covers it in only 8 percent of cases.
  • Data collection methods for speech safety benchmarks must address participant privacy concerns to remain viable.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training pipelines that rely mainly on English text may be leaving non-English speech safety unaddressed by default.
  • The same benchmark could be reused to track whether future model updates close the spoken-input gap over time.
  • Real-world deployment teams might need separate refusal policies for voice interfaces in languages where gaps are largest.

Load-bearing premise

The specific requests and recording conditions in RedVox match the unsafe and unfair content real users would actually send to these models in the five languages.

What would settle it

Running the same models on a fresh collection of unsafe requests drawn directly from real non-English users and finding no rise in failure rates relative to English or to text inputs would contradict the reported pattern.

Figures

Figures reproduced from arXiv: 2606.26968 by Beatrice Savoldi, Luisa Bentivogli, Matteo Negri, Sara Papi, Wafa Aissa.

Figure 1
Figure 1. Figure 1: Safety evaluation practices across 11 speech [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: REDVOX Framework. (Top) Benchmark properties and the two request types about safety and fairness. In Request Type I (Speech), harmful content is vocalized and accompanied by a textual follow-up request; in Request Type II (Audio), harmful content appears in text only, paired with a distracting audio signal. (Bottom) Evaluation workflow assessing model responses on an increasing severity scale. speech model… view at source ↗
Figure 3
Figure 3. Figure 3: Composition of REDVOX released data. En￾glish represents the largest portion (40%). Vulnerabil￾ity types (middle ring) are distinguished across unsafe (69%) and stereotypical requests (31%). Each audio type and speech recordings amount to ∼25% each. Chi-squared tests on categorical evaluation labels (see evaluation in the upcoming Section 4.1) further confirm robustness, with Cramér’s V remaining negligibl… view at source ↗
Figure 4
Figure 4. Figure 4: Mutlilingual results. Ratios of responses by ■ safe-by-accident, ■ controversial, ■ unsafe. 0 5 10 15 20 Unsafe Controversial Safe by Accident 0 5 10 15 20 Unsafe Controversial Safe by Accident 0 5 10 15 20 Unsafe Controversial Safe by Accident % 0 5 10 15 20 Unsafe Controversial Safe by Accident [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Ratio of responses by fairness (•) and safety (■). Non-English statistics are solid symbols, while English are transparent symbols. GPT-realtime2 Gemini-Flash-Lite3.1 Gemini-Pro3.1 Qwen3-Omni Gemma4 Qwen2-Audio Phi4-Multi Voxtral text noise-a noise-b silence speech 3% 18% 16% 10% 10% 21% 11% 22% 2% 11% 16% 9% 17% 23% 28% 41% 2% 12% 14% 11% 14% 24% 29% 36% 3% 12% 16% 9% 19% 23% 30% 42% 10% 15% 19% 15% 21% 2… view at source ↗
Figure 6
Figure 6. Figure 6: Ratio of controversial and unsafe (C+U) re￾sponses across textual, audio and speech inputs. only one type of audio leads to an increase—we can conclude that the mere presence of audio in￾put acts as a stressing factor for non-proprietary models, independent of semantic content. 6 Discussion Results on REDVOX confirm that safety and fair￾ness vulnerabilities in speech models are a present concern—not only u… view at source ↗
Figure 7
Figure 7. Figure 7: Participant attitudes. (A) Mean comfort (1– 5) with creating and releasing generic or harmful content in text vs. voice. (B) Ratio of participants reporting feeling personally responsible in pronouncing harmful requests and concerned about voice identification. related research).15 Figure 7B sheds light on such rates, highlighting both psychological and ethical as well as privacy concerns. The majority of … view at source ↗
Figure 8
Figure 8. Figure 8: Prompt used for LLM-as-a-Judge evaluation with [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Ratio of controversial and unsafe/unfair responses by audio type. Responses are provided by language [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Ratio of participants’ responses to: Which vulnerability categories felt most uncomfortable to en￾gage with? [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of model responses for native vs. non-native English speakers across eight audio￾language models (speech inputs only). Statistical testing was performed using one-sided Mann–Whitney U tests on the full ordinal outcome scale (safe related < safe unrelated < controversial < unsafe/unfair), testing the directional hypothesis that non-native speakers receive systematically worse outcomes than nat… view at source ↗
Figure 12
Figure 12. Figure 12: Distribution of model responses for men vs. women speakers across eight audio-language models (all langs, speech inputs only). Statistical testing was performed using two-sided Mann–Whitney U tests on the full ordinal outcome scale (safe related < safe unrelated < controversial < unsafe/unfair), testing whether any significant gendered difference exists. Effect size is reported as rank-biserial correlatio… view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper surveys safety and fairness reporting practices in state-of-the-art speech model releases (finding only 8% include multilingual analysis), introduces the RedVox benchmark of unsafe and unfair stereotypical requests across five languages (English, French, Italian, Spanish, German) using real voices, evaluates eight models under non-adversarial conditions, and reports that vulnerabilities persist, worsen in non-English languages, and are amplified with spoken inputs; it also discusses participant challenges in speech data collection.

Significance. If the benchmark requests are validated for equivalent severity across languages, the results would document an important and understudied gap in multilingual speech-model safety, directly addressing the low rate of multilingual analysis in model releases and highlighting risks for real-world spoken deployments.

major comments (2)
  1. [Abstract / benchmark construction] Abstract and benchmark description: the central claim that vulnerabilities 'worsen in non-English languages' is load-bearing on the assumption that unsafe/unfair requests maintain matched harm levels and stereotypical severity across the five languages. The manuscript supplies no details on request selection criteria, localization/translation validation, native-speaker harm ratings, back-translation checks, or cultural adaptation; without these, observed language differences may be confounded by prompt difficulty rather than model behavior.
  2. [Evaluation and results] Evaluation section: the abstract and results claim amplification under spoken inputs and persistence under non-adversarial conditions, yet no information is given on model versions, statistical methods, or how spoken vs. text inputs were controlled, preventing assessment of whether the data support the multilingual and modality claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment below and will revise the manuscript to incorporate additional details where the current version is insufficient.

read point-by-point responses

  1. Referee: [Abstract / benchmark construction] Abstract and benchmark description: the central claim that vulnerabilities 'worsen in non-English languages' is load-bearing on the assumption that unsafe/unfair requests maintain matched harm levels and stereotypical severity across the five languages. The manuscript supplies no details on request selection criteria, localization/translation validation, native-speaker harm ratings, back-translation checks, or cultural adaptation; without these, observed language differences may be confounded by prompt difficulty rather than model behavior.

    Authors: We agree that the manuscript lacks sufficient detail on benchmark construction to fully support the multilingual claims. In revision we will add a dedicated subsection describing request selection criteria, the translation and localization process, any back-translation or native-speaker validation steps performed, and steps taken to align stereotypical severity across languages. These additions will clarify the methodology and reduce the possibility of confounding by prompt difficulty. revision: yes

  2. Referee: [Evaluation and results] Evaluation section: the abstract and results claim amplification under spoken inputs and persistence under non-adversarial conditions, yet no information is given on model versions, statistical methods, or how spoken vs. text inputs were controlled, preventing assessment of whether the data support the multilingual and modality claims.

    Authors: We acknowledge that the evaluation section omits key methodological details. We will expand it to specify the exact model versions evaluated, the statistical tests and significance thresholds used for cross-language and cross-modality comparisons, and the precise controls applied to ensure spoken and text inputs were matched in content and delivery. These revisions will enable readers to assess the strength of the reported findings. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical benchmark evaluation is self-contained

full rationale

The paper introduces the RedVox benchmark and reports direct model evaluations across languages. No derivations, fitted parameters renamed as predictions, self-definitional quantities, or load-bearing self-citations appear in the provided text. The central claims rest on new data collection and model testing rather than reducing to prior inputs by construction. This matches the default expectation of non-circular empirical work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no free parameters, axioms, or invented entities can be identified from the full text.

pith-pipeline@v0.9.1-grok · 5699 in / 1006 out tokens · 39960 ms · 2026-06-26T04:34:01.234296+00:00 · methodology

discussion (0)

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