arXiv:2608. 09288v1 Announce Type: cross Abstract: Audio-visual speech enhancement under real-world conditions remains challenging due to unreliable visual inputs and the lack of large-scale training data with realistic acoustic conditions.
By Wei Zhou, Wanyi Ning, Yinshang Guo, Qianxiao Fang, Haitao Qian, Yingpeng Li
arXiv:2606. 23712v1 Announce Type: cross Abstract: Audio-visual speech enhancement (AVSE) exploits visual cues such as lip movements to recover speech in noisy environments.
By Colombe Mboungou (MULTISPEECH), Mostafa Sadeghi (MULTISPEECH), Jean-Eudes Ayilo (MULTISPEECH), Romain Serizel (MULTISPEECH)
arXiv:2609.30631v1 Announce Type: cross
Abstract: Online audio-visual target-speaker extraction aims to remove competing voices while preserving speech quality and bounding lookahead. Existing extrac...
By Rayhan Rashed, Senja Filipi, Ross Cutler
arXiv:2609.07226v1 Announce Type: cross
Abstract: This paper proposes a general framework for stable and effective iterative audio separation with mixture consistency by extending source separation m...
By Yukara Ikemiya, WeiHsiang Liao, Yuki Mitsufuji
arXiv:2604. 01832v1 Announce Type: cross Abstract: We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge.
By Xiaobin Rong, Yushi Wang, Zheng Wang, Jing Lu
arXiv:2609.26648v1 Announce Type: cross
Abstract: Active speaker detection (ASD) requires reliable association between visible faces and acoustic speech, yet existing systems often degrade under chal...
By Pu Wang, Yujun Wang, Hugo Van hamme