Voice or Stereotype? Disentangling Acoustic and Content-Based Gender in Speech-to-Speech Models — Paper

Abstract

Speech-to-speech (S2S) models now run inside dubbing, translation, and voice agents. Unlike text models, they hear the speaker's voice, which carries the speaker's gender. A faithful system should treat a speaker as who they sound like, not as whoever usually says what they said. Testing this is harder than it looks, since most S2S models answer in a single, fixed output voice, hard-coded so it cannot drift toward a stereotype. Checking the output voice comes back clean even when the model is biased. We therefore ask two questions. When a model re-speaks the input, does the stereotype in the words shift the perceived gender of the output voice (voice rendering)? And when the model states the speaker's gender, does it follow the voice or the content (gender attribution)? We answer both with one controlled experiment crossing male and female voices with masculine-, neutral-, and feminine-stereotyped passages, on five open- and closed-source models in English, Spanish, and Mandarin. The rendered voice shows no stereotype drift. But every model decides the speaker's gender from the content, not the voice. Making the content one step more feminine (masculine -> neutral -> feminine) multiplies the odds of a "female" judgment by 1.7-24. When the content clashes with the voice, the worst model misgenders the speaker in 90% of cases. When they agree, it misgenders in only 2%. The bias thus hides in gender attribution, where fixed-voice evaluation cannot see, and where audits must look as S2S systems increasingly speak for real people.

Authors 4

Xiaoqun Liu, Tanu Mitra, Harshit Rajgarhia, Abhishek Mukherji

Specification

Official page: arxiv.org/abs/2609.09263
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
arXiv id: 2609.09263
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Categories: cs.SD, cs.AI
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
PDF: arxiv.org/pdf/2609.09263
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Primary category: cs.SD
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh

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Attributed facts

9

Source tiers

T19

Freshest observation

4 h ago

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None

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Authors: Xiaoqun Liu, Tanu Mitra, Harshit Rajgarhia

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Claim history for Authors
Value	Valid from → to	Status	Source	Confidence	Extractor
Xiaoqun Liu, Tanu Mitra, Harshit Rajgarhia, Abhishek Mukherji	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperVoice or Stereotype? Disentangling Acoustic and Content-Based Gender in Speech-to-Speech Models
New paper: Voice or Stereotype? Disentangling Acoustic and Content-Based Gender in Speech-to-Speech Models
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	2 h ago	1

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