Exploring Cross-Lingual Voice Conversion Methods for Anonymizing Low-Resource Text-to-Speech

AbstractWe 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 SENĆOŦEN, 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