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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.
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.
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...
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.
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.
arXiv:2606. 06559v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models allow voice agents to listen and speak concurrently, enabling natural interaction with real-time overlap.