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Speech-to-Speech Models Show Gender Bias in Content, Not Voice

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates gender bias in speech-to-speech (S2S) models, finding that these models attribute gender based on content rather than the speaker's actual voice. Researchers tested five open- and closed-source models across English, Spanish, and Mandarin, using passages with varying gender stereotypes. While the models did not alter the output voice to match stereotypes, they consistently misattributed gender when the spoken content conflicted with the speaker's voice, with some models making incorrect gender judgments in up to 90% of cases. AI

IMPACT Reveals a critical bias in speech-to-speech models that could impact user trust and equitable application.

RANK_REASON The cluster contains a research paper published on arXiv detailing findings about bias in AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Speech-to-Speech Models Show Gender Bias in Content, Not Voice

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The cluster contains a research paper published on arXiv detailing findings about bias in AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiaoqun Liu, Tanu Mitra, Harshit Rajgarhia, Abhishek Mukherji ·

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

    arXiv:2609.09263v1 Announce Type: cross 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…

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