30 citations · 30 across the 10 of their papers we have counts for
11 papers
The VoiceMOS Challenge 2026: Evaluating Speech Enhancement, Emotional TTS and Accented TTS Systems
Wen-Chin Huang, Wei Wang, Marvin Sach +4
We present the results of the VoiceMOS Challenge 2026, the fifth edition of a scientific challenge on automatic prediction of subjective speech assessments. After expanding the sco…
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…
Non-Causal to Causal SSL-Supported Transfer Learning: Towards a High-Performance Low-Latency Speech Vocoder
Renzheng Shi, Andreas Bär, Marvin Sach +2
Recently, BigVGAN has emerged as high-performance speech vocoder. Its sequence-to-sequence-based synthesis, however, prohibits usage in low-latency conversational applications. Our…
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-…
Employing Real Training Data for Deep Noise Suppression
Ziyi Xu, Marvin Sach, Jan Pirklbauer +1
Most deep noise suppression (DNS) models are trained with reference-based losses requiring access to clean speech. However, sometimes an additive microphone model is insufficient f…
EffCRN: An Efficient Convolutional Recurrent Network for High-Performance Speech Enhancement
Marvin Sach, Jan Franzen, Bruno Defraene +4
Fully convolutional recurrent neural networks (FCRNs) have shown state-of-the-art performance in single-channel speech enhancement. However, the number of parameters and the FLOPs/…