@inproceedings{wang-etal-2026-exploring,
    title = "Exploring Cross-Lingual Voice Conversion Methods for Anonymizing Low-Resource Text-to-Speech",
    author = "Wang, Shenran  and
      Pine, Aidan  and
      Geng, Mengzhe",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-short.16/",
    pages = "225--231",
    ISBN = "979-8-89176-381-4",
    abstract = "We describe and compare multiple approaches for using voice conversion techniques to mask speaker identities in low-resource text-to-speech. We build and evaluate speaker-anonymized text-to-speech systems for two Canadian Indigenous languages, n{\^e}hiyaw{\^e}win and SEN{\'C}O{\={T}}EN, and show that cross-lingual speaker transfer via multilingual training with English data produces the most consistent results across both languages. Our research also underscores the need for better evaluation metrics tailored to cross-lingual voice conversion. Our code can be found at https://github.com/EveryVoiceTTS/Speaker{\_}Anonymization{\_}StyleTTS2"
}