噪声下的神经语音跟踪反映了信噪比对可理解性和注意力努力的相反影响。

Imaging neuroscience (Cambridge, Mass.) Pub Date : 2025-08-28 eCollection Date: 2025-01-01 DOI:10.1162/IMAG.a.126
Xiaomin He, Vinay S Raghavan, Nima Mesgarani
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引用次数: 0

摘要

理解噪声中的语音取决于几个相互作用的因素,包括信噪比(SNR)、语音可理解性(SI)和注意力参与。然而,这些因素与选择性神经语音跟踪的关系尚不清楚。在这项研究中,我们记录了参与者在进行选择性听力任务时的脑电图和眼动追踪数据,该任务涉及一个目标说话者,在一个竞争的掩蔽说话者和一个大范围信噪比的背景噪音的存在下。我们的研究结果揭示了一种非线性关系,即目标语音的神经跟踪首先随着信噪比的增加而增加,然后随着信噪比的继续提高而矛盾地减少。为了解释这一点,我们量化了SI行为,使用凝视速度估计了注意力努力(AE),并通过重复单词检测任务测量了行为表现(BP)。我们的分析表明,目标语音的神经跟踪随着SI和注意力的参与而增加。然而,当可理解性达到上限水平时,选择性神经语音跟踪随着声发射的下降而下降。统计模型表明,SI和AE是神经跟踪的可靠预测因子,而考虑这些因素后,信噪比没有独立贡献。我们的研究结果表明,提高信噪比对选择性神经语音跟踪的影响主要是通过增加SI和同时降低AE来实现的,而这两者对神经跟踪的影响是相反的。这些发现强调了在噪声环境下语音感知研究中联合考虑这些因素的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural speech tracking in noise reflects the opposing influence of SNR on intelligibility and attentional effort.

Understanding speech in noise depends on several interacting factors, including the signal-to-noise ratio (SNR), speech intelligibility (SI), and attentional engagement. However, how these factors relate to selective neural speech tracking remains unclear. In this study, we recorded EEG and eye-tracking data while participants performed a selective listening task involving a target talker in the presence of a competing masker talker and background noise across a wide range of SNRs. Our results revealed a non-linear relationship, where neural tracking of the target speech first increased with SNR but then paradoxically decreased as SNR continued to improve. To explain this, we quantified SI behaviorally, estimated attentional effort (AE) using gaze velocity, and measured behavioral performance (BP) via a repeated-word detection task. Our analysis showed that neural tracking of the target speech increased with both SI and attentional engagement. However, when intelligibility reached ceiling levels, selective neural speech tracking decreased as AE declined. Statistical modeling indicated that SI and AE were reliable predictors of neural tracking, while SNR showed no independent contribution after accounting for these factors. Our results demonstrate that improved SNR influences selective neural speech tracking primarily by increasing SI and simultaneously reducing AE, which have opposing effects on neural tracking. These findings underscore the importance of jointly considering these factors in studies of speech perception in noise.

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