17 citations · 49 across the 24 of their papers we have counts for
8 papers · 1 filter
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision
Yafeng Chen, Siqi Zheng, Hui Wang +4
Training speaker-discriminative and robust speaker verification systems without explicit speaker labels remains a persisting challenge. In this paper, we propose a new self-supervi…
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision
Yafeng Chen, Siqi Zheng, Hui Wang +5
Training speaker-discriminative and robust speaker verification systems without explicit speaker labels remains a persistent challenge. In this paper, we propose a novel self-super…
An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification
Yafeng Chen, Siqi Zheng, Hui Wang +3
Effective fusion of multi-scale features is crucial for improving speaker verification performance. While most existing methods aggregate multi-scale features in a layer-wise manne…
DopplerBAS: Binaural Audio Synthesis Addressing Doppler Effect
Jinglin Liu, Zhenhui Ye, Qian Chen +4
Recently, binaural audio synthesis (BAS) has emerged as a promising research field for its applications in augmented and virtual realities. Binaural audio helps users orient themse…
Contextual Expressive Text-to-Speech
Jianhong Tu, Zeyu Cui, Xiaohuan Zhou +4
The goal of expressive Text-to-speech (TTS) is to synthesize natural speech with desired content, prosody, emotion, or timbre, in high expressiveness. Most of previous studies atte…
Pushing the limits of self-supervised speaker verification using regularized distillation framework
Yafeng Chen, Siqi Zheng, Hui Wang +2
Training robust speaker verification systems without speaker labels has long been a challenging task. Previous studies observed a large performance gap between self-supervised and…
Deep Representation Decomposition for Rate-Invariant Speaker Verification
Fuchuan Tong, Siqi Zheng, Haodong Zhou +3
While promising performance for speaker verification has been achieved by deep speaker embeddings, the advantage would reduce in the case of speaking-style variability. Speaking ra…
Graph Convolutional Network Based Semi-Supervised Learning on Multi-Speaker Meeting Data
Fuchuan Tong, Siqi Zheng, Min Zhang +4
Unsupervised clustering on speakers is becoming increasingly important for its potential uses in semi-supervised learning. In reality, we are often presented with enormous amounts…