发表机构
Southern University of Science and Technology; Capital Medical University（南方科技大学; 首都医科大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对试次内听觉注意切换下的脑电引导目标说话人提取难题，提出SAGE框架，通过软门控等技术提升性能，在多项指标上优于基线，实现了动态场景下的稳健目标提取。
AI 中文摘要
在试次内听觉注意切换场景下，脑电引导的目标说话人提取面临挑战，神经噪声与固有延迟会导致注意跟踪延迟或不稳定，传统方法难以应对动态切换，常在切换点产生不连续问题。因此，我们提出SAGE，一种开关感知的脑电引导软门控框架，将试次内切换视为动态选择。SAGE借助鲁棒分离器生成两个候选语音流，通过脑电引导的开关感知门控模块生成平滑融合权重并抑制过渡伪影；我们进一步整合延迟补偿对齐与不确定性驱动的保守策略，以处理延迟差异与脑电可靠性波动。SAGE的性能优于基线方法，取得8.67 dB的SI-SDR与88.24%的STOI，同时将平均切换延迟降至2.04 s，通过将神经解码与语音分离相结合，实现了动态场景下的稳健目标提取。
英文摘要
EEG-guided target speaker extraction is challenging under in-trial auditory attention switching, where neural noise and intrinsic latency can delay or destabilize attention tracking. Conventional methods struggle with dynamic switches and often cause discontinuities at switching points. Therefore, we propose SAGE, a switch-aware EEG-guided soft gating framework that treats in-trial switching as dynamic selection. SAGE generates two candidate speech streams with a robust separator and uses an EEG-guided switch-aware gating module to produce smooth fusion weights and suppress transition artifacts. We further integrate latency-compensated alignment and an uncertainty-driven conservative strategy to handle latency discrepancies and fluctuating EEG reliability. SAGE outperforms baselines, achieving 8.67 dB SI-SDR and 88.24% STOI while reducing average switching latency to 2.04 s. By coupling neural decoding with speech separation, it enables robust target extraction in dynamic scenarios.
