7 citations · 14 across the 33 of their papers we have counts for
5 papers · 1 filter
Heterogeneous Space Fusion and Dual-Dimension Attention: A New Paradigm for Speech Enhancement
Tao Zheng, Liejun Wang, Yinfeng Yu
Self-supervised learning has demonstrated impressive performance in speech tasks, yet there remains ample opportunity for advancement in the realm of speech enhancement research. I…
VNet: A GAN-based Multi-Tier Discriminator Network for Speech Synthesis Vocoders
Yubing Cao, Yongming Li, Liejun Wang +1
Since the introduction of Generative Adversarial Networks (GANs) in speech synthesis, remarkable achievements have been attained. In a thorough exploration of vocoders, it has been…
BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised Embedding
Alimjan Mattursun, Liejun Wang, Yinfeng Yu
Speech self-supervised learning (SSL) represents has achieved state-of-the-art (SOTA) performance in multiple downstream tasks. However, its application in speech enhancement (SE)…
PCQ: Emotion Recognition in Speech via Progressive Channel Querying
Xincheng Wang, Liejun Wang, Yinfeng Yu +1
In human-computer interaction (HCI), Speech Emotion Recognition (SER) is a key technology for understanding human intentions and emotions. Traditional SER methods struggle to effec…
MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention
Xinxin Jiao, Liejun Wang, Yinfeng Yu
Speech emotion recognition is crucial in human-computer interaction, but extracting and using emotional cues from audio poses challenges. This paper introduces MFHCA, a novel metho…