The paper introduces CF-AVSS, a two‑stage audio‑visual speech separation framework that first generates coarse estimates using a synchronized visual‑stream SPAV‑TFGridNet, then refines these estimates with a TFGridNet‑based target‑speaker refinement network. The final output is a fusion of coarse and refined estimates calibrated on a development set. In the ISCSLP 2026 Real‑World AVSE Challenge, the system tied for first place on Track 1 and ranked second on Track 2, achieving the lowest character error rates of 12.31 % and 14.79 % respectively.
By Longjie Luo, Zhongshu Hou, Yuepeng Li, Gang Yang, Shidong Shang, Fan Zhou
Audio-visual speech enhancement (AVSE) leverages visual cues to recover target speech from mixtures corrupted by reverberation, noise, and overlapping speakers. We propose a coarse-to-fine two-stage A...
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:2602. 01394v2 Announce Type: replace-cross Abstract: This paper addresses the challenge of audio-visual single-microphone speech separation and enhancement in the presence of real-world environmental noise.
By Yochai Yemini, Yoav Ellinson, Rami Ben-Ari, Sharon Gannot, Ethan Fetaya
arXiv:2609.25864v1 Announce Type: cross
Abstract: Visual object removal can eliminate a target from video frames, yet its acoustic trace persists in the soundtrack, causing obvious audio-visual incon...
By Xinyue Guo, Jianxuan Yang, Daiguo Zhou, Jiagao Hu, Yuxuan Chen, Fei Wang, Jian Luan
arXiv:2510. 20441v2 Announce Type: replace-cross Abstract: Neural audio codecs have largely promoted the application of language models (LMs) for speech applications.
By Haoyin Yan, Chengwei Liu, Shaofei Xue, Xiaotao Liang, Yinghao Liu, Yuxiang Kong, Zheng Xue