Yingda Shen

Yudong Li, Zihao Fang, Junwen Qiu et al.

Streaming zero-shot voice conversion struggles to disentangle timbre from linguistic content without degrading utility or inflating latency. Current methods rely on information bottleneck (IB) or speaker perturbation. While IB filters out timbre, it discards prosody, forcing models to explicitly inject features like fundamental frequency. This often requires buffering future frames, creating algorithmic lookahead latency. On the other hand, existing perturbation methods largely overlook the crucial trade-off between timbre leakage and utility preservation. Recognizing this neglected trade-off, we find that the inherent objective of Speaker Anonymization (SA) aligns well with balancing these factors. Thus, we introduce SA as a novel perturbation mechanism to explicitly mitigate timbre leakage while retaining prosodic utility. Crucially, SA's robust representations significantly alleviate the generator's reliance on future context, enabling our strictly causal, zero-lookahead network. Audio samples are available at https://amphionteam.github.io/Zero-VC-demo/.

Zihao Fang, Yingda Shen, Zifan Guan et al.

Whispered speech lacks vocal fold vibration and fundamental frequency, resulting in degraded acoustic cues and making whisper-to-normal (W2N) conversion challenging, especially with limited parallel data. We propose WhispEar, a bidirectional framework based on unified semantic representations that capture speaking-mode-invariant information shared by whispered and normal speech. The framework contains both W2N and normal-to-whisper (N2W) models. Notably, the N2W model enables zero-shot pseudo-parallel whisper generation from abundant normal speech, allowing scalable data augmentation for W2N training. Increasing generated data consistently improves performance. We also release the largest bilingual (Chinese-English) whispered-normal parallel corpus to date. Experiments demonstrate that WhispEar outperforms strong baselines and benefits significantly from scalable pseudo-parallel data.