The integration of continuous audio and visual speech in a cocktail-party environment depends on attention

多传感器集成 语音识别 计算机科学 语音处理 任务(项目管理) 自然(考古学) 神经计算语音处理 心理学 言语感知 认知心理学 感觉系统 感知 神经科学 管理 考古 经济 历史
作者
Farhin Ahmed,Aaron Nidiffer,Aisling E. O’Sullivan,Nathaniel J. Zuk,Edmund C. Lalor
出处
期刊:NeuroImage [Elsevier BV]
卷期号:274: 120143-120143 被引量:4
标识
DOI:10.1016/j.neuroimage.2023.120143
摘要

In noisy environments, our ability to understand speech benefits greatly from seeing the speaker's face. This is attributed to the brain's ability to integrate audio and visual information, a process known as multisensory integration. In addition, selective attention plays an enormous role in what we understand, the so-called cocktail-party phenomenon. But how attention and multisensory integration interact remains incompletely understood, particularly in the case of natural, continuous speech. Here, we addressed this issue by analyzing EEG data recorded from participants who undertook a multisensory cocktail-party task using natural speech. To assess multisensory integration, we modeled the EEG responses to the speech in two ways. The first assumed that audiovisual speech processing is simply a linear combination of audio speech processing and visual speech processing (i.e., an A + V model), while the second allows for the possibility of audiovisual interactions (i.e., an AV model). Applying these models to the data revealed that EEG responses to attended audiovisual speech were better explained by an AV model, providing evidence for multisensory integration. In contrast, unattended audiovisual speech responses were best captured using an A + V model, suggesting that multisensory integration is suppressed for unattended speech. Follow up analyses revealed some limited evidence for early multisensory integration of unattended AV speech, with no integration occurring at later levels of processing. We take these findings as evidence that the integration of natural audio and visual speech occurs at multiple levels of processing in the brain, each of which can be differentially affected by attention.
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