7 papers
Gen-SER: When the generative model meets speech emotion recognition
Taihui Wang, Jinzheng Zhao, Rilin Chen +3
Speech emotion recognition (SER) is crucial in speech understanding and generation. Most approaches are based on either classification models or large language models. Different fr…
BridgeVoC: Revitalizing Neural Vocoder from a Restoration Perspective
Andong Li, Tong Lei, Rilin Chen +5
This paper revisits the neural vocoder task through the lens of audio restoration and propose a novel diffusion vocoder called BridgeVoC. Specifically, by rank analysis, we compare…
Target matching based generative model for speech enhancement
Taihui Wang, Rilin Chen, Tong Lei +4
The design of mean and variance schedules for the perturbed signal is a fundamental challenge in generative models. While score-based and Schrödinger bridge-based models require ca…
Learning Neural Vocoder from Range-Null Space Decomposition
Andong Li, Tong Lei, Zhihang Sun +4
Despite the rapid development of neural vocoders in recent years, they usually suffer from some intrinsic challenges like opaque modeling, and parameter-performance trade-off. In t…
From Continuous to Discrete: Cross-Domain Collaborative General Speech Enhancement via Hierarchical Language Models
Zhaoxi Mu, Rilin Chen, Andong Li +3
This paper introduces OmniGSE, a novel general speech enhancement (GSE) framework designed to mitigate the diverse distortions that speech signals encounter in real-world scenarios…
FNSE-SBGAN: Far-field Speech Enhancement with Schrodinger Bridge and Generative Adversarial Networks
Tong Lei, Qinwen Hu, Ziyao Lin +5
The prevailing method for neural speech enhancement predominantly utilizes fully-supervised deep learning with simulated pairs of far-field noisy-reverberant speech and clean speec…