30 citations · 45 across the 12 of their papers we have counts for
12 papers
UrgentMOS: Unified Multi-Metric and Preference Learning for Robust Speech Quality Assessment
Wei Wang, Wangyou Zhang, Chenda Li +12
Automatic speech quality assessment has become increasingly important as modern speech generation systems continue to advance, while human listening tests remain costly, time-consu…
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…
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…
Interspeech 2025 URGENT Speech Enhancement Challenge
Kohei Saijo, Wangyou Zhang, Samuele Cornell +9
There has been a growing effort to develop universal speech enhancement (SE) to handle inputs with various speech distortions and recording conditions. The URGENT Challenge series…
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…
A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction
Kohei Saijo, Wangyou Zhang, Zhong-Qiu Wang +3
We propose a multi-task universal speech enhancement (MUSE) model that can perform five speech enhancement (SE) tasks: dereverberation, denoising, speech separation (SS), target sp…
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Xuankai Chang, Brian Yan, Kwanghee Choi +14
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling. High-dimensional speech features…
Exploring Speech Enhancement for Low-resource Speech Synthesis
Zhaoheng Ni, Sravya Popuri, Ning Dong +6
High-quality and intelligible speech is essential to text-to-speech (TTS) model training, however, obtaining high-quality data for low-resource languages is challenging and expensi…
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…