Speech translation models learn masculine bias, override with acoustic cues

A new study published on arXiv investigates how speech translation models assign gender to speaker-referring terms. Researchers found that these models learn broader patterns of masculine prevalence beyond simple term associations from training data. While the internal language model shows a strong masculine bias, acoustic input can influence gender assignment. The study identified a novel mechanism where models use first-person pronouns to link gendered terms to the speaker, extracting gender information from the frequency spectrum. AI

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IMPACT Highlights potential for gender bias in speech translation models and identifies novel mechanisms for gender assignment.

RANK_REASON Academic paper published on arXiv detailing research findings.

Read on arXiv cs.CL →

• arXiv
• Lina Conti
• Hugging Face

• paper
• safety