30 citations · 53 across the 20 of their papers we have counts for
13 papers · 1 filter
Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution
Zhenglong Liu, Wangyou Zhang, Chenda Li +1
Multi-channel speech enhancement (SE) systems exhibit superior performance over single-channel methods but are constrained to fixed microphone array configurations. This restricts…
Representation-Regularized Convolutional Audio Transformer for Audio Understanding
Bing Han, Chushu Zhou, Yifan Yang +4
Bootstrap-based Self-Supervised Learning (SSL) has achieved remarkable progress in audio understanding. However, existing methods typically operate at a single level of granularity…
ICASSP 2026 URGENT Speech Enhancement Challenge
Chenda Li, Wei Wang, Marvin Sach +8
The ICASSP 2026 URGENT Challenge advances the series by focusing on universal speech enhancement (SE) systems that handle diverse distortions, domains, and input conditions. This o…
MeanSE: Efficient Generative Speech Enhancement with Mean Flows
Jiahe Wang, Hongyu Wang, Wei Wang +5
Speech enhancement (SE) improves degraded speech's quality, with generative models like flow matching gaining attention for their outstanding perceptual quality. However, the flow-…
P.808 Multilingual Speech Enhancement Testing: Approach and Results of URGENT 2025 Challenge
Marvin Sach, Yihui Fu, Kohei Saijo +9
In speech quality estimation for speech enhancement (SE) systems, subjective listening tests so far are considered as the gold standard. This should be even more true considering t…
URGENT-PK: Perceptually-Aligned Ranking Model Designed for Speech Enhancement Competition
Jiahe Wang, Chenda Li, Wei Wang +11
The Mean Opinion Score (MOS) is fundamental to speech quality assessment. However, its acquisition requires significant human annotation. Although deep neural network approaches, s…
Lessons Learned from the URGENT 2024 Speech Enhancement Challenge
Wangyou Zhang, Kohei Saijo, Samuele Cornell +10
The URGENT 2024 Challenge aims to foster speech enhancement (SE) techniques with great universality, robustness, and generalizability, featuring a broader task definition, large-sc…
Scale This, Not That: Investigating Key Dataset Attributes for Efficient Speech Enhancement Scaling
Leying Zhang, Wangyou Zhang, Chenda Li +1
Recent speech enhancement models have shown impressive performance gains by scaling up model complexity and training data. However, the impact of dataset variability (e.g. text, la…
Diffusion-based Generative Modeling with Discriminative Guidance for Streamable Speech Enhancement
Chenda Li, Samuele Cornell, Shinji Watanabe +1
Diffusion-based generative models (DGMs) have recently attracted attention in speech enhancement research (SE) as previous works showed a remarkable generalization capability. Howe…
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement
Wangyou Zhang, Robin Scheibler, Kohei Saijo +9
The last decade has witnessed significant advancements in deep learning-based speech enhancement (SE). However, most existing SE research has limitations on the coverage of SE sub-…
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech Enhancement
Wangyou Zhang, Kohei Saijo, Jee-weon Jung +3
Deep learning-based speech enhancement (SE) models have achieved impressive performance in the past decade. Numerous advanced architectures have been designed to deliver state-of-t…
Improving Design of Input Condition Invariant Speech Enhancement
Wangyou Zhang, Jee-weon Jung, Shinji Watanabe +1
Building a single universal speech enhancement (SE) system that can handle arbitrary input is a demanded but underexplored research topic. Towards this ultimate goal, one direction…
Toward Universal Speech Enhancement for Diverse Input Conditions
Wangyou Zhang, Kohei Saijo, Zhong-Qiu Wang +2
The past decade has witnessed substantial growth of data-driven speech enhancement (SE) techniques thanks to deep learning. While existing approaches have shown impressive performa…