In natural conversations, listeners must attend to what others are saying while ignoring extraneous background sounds. Recent studies have used encoding models to predict electroencephalography (EEG) responses to speech in noise-free listening situations, sometimes referred to as "speech tracking" in EEG. Researchers have analyzed how speech tracking changes with different types of background noise. It is unclear, however, whether neural responses from noisy and naturalistic environments can be generalized to more controlled stimuli. If encoding models for noisy, naturalistic stimuli are generalizable to other tasks, this could aid in data collection from populations who may not tolerate listening to more controlled, less-engaging stimuli for long periods of time. We recorded non-invasive scalp EEG while participants listened to speech without noise and audiovisual speech stimuli containing overlapping speakers and background sounds. We fit multivariate temporal receptive field (mTRF) encoding models to predict EEG responses to pitch, the acoustic envelope, phonological features, and visual cues in both noise-free and noisy stimulus conditions. Our results suggested that neural responses to naturalistic stimuli were generalizable to more controlled data sets. EEG responses to speech in isolation were predicted accurately using phonological features alone, while responses to noisy speech were more accurate when including both phonological and acoustic features. These findings may inform basic science research on speech-in-noise processing. Ultimately, they may also provide insight into auditory processing in people who are hard of hearing, who use a combination of audio and visual cues to understand speech in the presence of noise.

中文翻译：

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具有自然视听刺激的通用化EEG编码模型

在自然对话中，听众必须注意别人在说什么，而忽略无关的背景声音。最近的研究已使用编码模型来预测在无噪声聆听情况下对语音的脑电图（EEG）反应，有时在EEG中称为“语音跟踪”。研究人员分析了语音跟踪如何随不同类型的背景噪声而变化。但是，尚不清楚是否可以将来自嘈杂和自然主义环境的神经反应推广到更可控的刺激中。如果将嘈杂的，自然的刺激的编码模型推广到其他任务，这可能有助于从人群中收集数据，这些人群可能长时间不愿听取更多受控，较少参与的刺激。我们记录了非侵入性头皮脑电图，而参与者听了无噪音的语音和包含重叠扬声器和背景声音的视听语音刺激。我们拟合多元时间感受野（mTRF）编码模型，以预测在无噪声和嘈杂刺激条件下对音调，声学包络，语音特征和视觉提示的EEG反应。我们的研究结果表明，对自然刺激的神经反应可以推广到更多受控数据集。单独使用语音功能，可以准确预测孤立的EEG语音响应，而同时包含语音和声学功能时，对嘈杂语音的响应则更准确。这些发现可能会为基础的有关噪声中语音处理的科学研究提供参考。最终，