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Prime-EVC: One-Step Expressive Emotional Voice Conversion via Emotion-Primed Flow Matching
Yu Pan, Xiaojuan Cai, Liang Zhang +7
Recent diffusion- and conditional flow matching (CFM)-based emotional voice conversion (EVC) systems achieve strong conversion quality. However, they typically rely on multiple sam…
ClapFM-EVC: High-Fidelity and Flexible Emotional Voice Conversion with Dual Control from Natural Language and Speech
Yu Pan, Yanni Hu, Yuguang Yang +5
Despite great advances, achieving high-fidelity emotional voice conversion (EVC) with flexible and interpretable control remains challenging. This paper introduces ClapFM-EVC, a no…
Zero-Shot Voice Conversion via Content-Aware Timbre Ensemble and Conditional Flow Matching
Yu Pan, Yuguang Yang, Jixun Yao +2
Despite recent advances in zero-shot voice conversion (VC), achieving speaker similarity and naturalness comparable to ground-truth recordings remains a significant challenge. In t…
Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling
Yuguang Yang, Yu Pan, Jixun Yao +6
Expressive zero-shot voice conversion (VC) is a critical and challenging task that aims to transform the source timbre into an arbitrary unseen speaker while preserving the origina…
Takin: A Cohort of Superior Quality Zero-shot Speech Generation Models
Sijing Chen, Yuan Feng, Laipeng He +22
With the advent of the big data and large language model era, zero-shot personalized rapid customization has emerged as a significant trend. In this report, we introduce Takin Audi…
GMP-TL: Gender-augmented Multi-scale Pseudo-label Enhanced Transfer Learning for Speech Emotion Recognition
Yu Pan, Yuguang Yang, Heng Lu +2
The continuous evolution of pre-trained speech models has greatly advanced Speech Emotion Recognition (SER). However, current research typically relies on utterance-level emotion l…