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        <title>Exploring Cross-Lingual Voice Conversion Methods for Anonymizing Low-Resource Text-to-Speech</title>
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    <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, nêhiyawêwin and SENĆO\=TEN, 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</abstract>
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%0 Conference Proceedings
%T Exploring Cross-Lingual Voice Conversion Methods for Anonymizing Low-Resource Text-to-Speech
%A Wang, Shenran
%A Pine, Aidan
%A Geng, Mengzhe
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-381-4
%F wang-etal-2026-exploring
%X 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, nêhiyawêwin and SENĆO\=TEN, 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
%U https://aclanthology.org/2026.eacl-short.16/
%P 225-231

Markdown (Informal)

[Exploring Cross-Lingual Voice Conversion Methods for Anonymizing Low-Resource Text-to-Speech](https://aclanthology.org/2026.eacl-short.16/) (Wang et al., EACL 2026)

• Exploring Cross-Lingual Voice Conversion Methods for Anonymizing Low-Resource Text-to-Speech (Wang et al., EACL 2026)

ACL

• Shenran Wang, Aidan Pine, and Mengzhe Geng. 2026. Exploring Cross-Lingual Voice Conversion Methods for Anonymizing Low-Resource Text-to-Speech. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 225–231, Rabat, Morocco. Association for Computational Linguistics.