@inproceedings{fucci-etal-2025-different,
    title = "Different Speech Translation Models Encode and Translate Speaker Gender Differently",
    author = "Fucci, Dennis  and
      Gaido, Marco  and
      Negri, Matteo  and
      Bentivogli, Luisa  and
      Martins, Andre  and
      Attanasio, Giuseppe",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-short.78/",
    pages = "1005--1019",
    ISBN = "979-8-89176-252-7",
    abstract = "Recent studies on interpreting the hidden states of speech models have shown their ability to capture speaker-specific features, including gender. Does this finding also hold for speech translation (ST) models? If so, what are the implications for the speaker{'}s gender assignment in translation? We address these questions from an interpretability perspective, using probing methods to assess gender encoding across diverse ST models. Results on three language directions (English $\rightarrow$ French/Italian/Spanish) indicate that while traditional encoder-decoder models capture gender information, newer architectures{---}integrating a speech encoder with a machine translation system via adapters{---}do not. We also demonstrate that low gender encoding capabilities result in systems' tendency toward a masculine default, a translation bias that is more pronounced in newer architectures."
}