融合视听同步与人脸语音关联，提升第一视角视频中的说话人检测鲁棒性。

Audiovisual active speaker detection (ASD) in egocentric recordings is challenged by frequent occlusions, motion blur, and audio interference, which undermine the discernability of temporal synchrony between lip movement and speech. Traditional synchronisation-based systems perform well under clean conditions but degrade sharply in first-person recordings. Conversely, face-voice association (FVA)-based methods forgo synchronisation modelling in favour of cross-modal biometric matching, exhibiting robustness to transient visual corruption but suffering when overlapping speech or front-end segmentation errors occur. In this paper, a simple yet effective ensemble approach is proposed to fuse synchronisation-dependent and synchronisation-agnostic model outputs via weighted averaging, thereby harnessing complementary cues without introducing complex fusion architectures. A refined preprocessing pipeline for the FVA-based component is also introduced to optimise ensemble integration. Experiments on the Ego4D-AVD validation set demonstrate that the ensemble attains 70.2% and 66.7% mean Average Precision (mAP) with TalkNet and Light-ASD backbones, respectively. A qualitative analysis stratified by face image quality and utterance masking prevalence further substantiates the complementary strengths of each component.