Papers by Huan Vu

1 papers
O_O-VC: Synthetic Data-Driven One-to-One Alignment for Any-to-Any Voice Conversion (2025.findings-emnlp)

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Challenge: Traditional voice conversion methods attempt to separate speaker identity and linguistic information into distinct representations, but this method often leads to information loss during training.
Approach: They propose a method that leverages synthetic speech data generated by a pretrained model . synthetic data pairs that share the same linguistic content are used as input-output pairs .
Outcome: The proposed method outperforms state-of-the-art methods in speaker-to-voice conversions.

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