Year: 2026
Type: preprint
Abstract: Recent advances in zero-shot voice conversion have exhibited potential in emotion control, yet the performance is suboptimal or inconsistent due to their limited expressive capacity. We propose Emotion-Aware Prefix for explicit emotion control in a two-stage voice conversion backbone. We significantly improve emotion conversion performance, doubling the baseline Emotion Conversion Accuracy (ECA) from 42.40% to 85.50% while maintaining linguistic integrity and speech quality, without compromising speaker identity. Our ablation study suggests that a joint control of both sequence modulation and acoustic realization is essential to synthesize distinct emotions. Furthermore, comparative analysis verifies the generalizability of proposed method, while it provides insights on the role of acoustic decoupling in maintaining speaker identity.
Source: arXiv (Cornell University)
Institution: The University of Texas at Dallas
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7134935079