Predicting speech-in-noise ability with static and dynamic auditory figure-ground analysis using structural equation modelling

图-地面 噪音(视频) 语音识别 声学 计算机科学 心理学 人工智能 物理 感知 神经科学 图像(数学)
作者
Xiaoxuan Guo,Ester Benzaquén,Emma Holmes,Joel I. Berger,Inga Brühl,William Sedley,Steven Rushton,Timothy D. Griffiths
标识
DOI:10.1101/2024.09.08.611859
摘要

Auditory figure-ground paradigms assess the ability to extract a foreground figure from a random background, a crucial part of central hearing. Previous studies have shown that the ability to extract static figures (with fixed frequencies) predicts real-life listening: speech-in-noise ability. In this study we assessed both fixed and dynamic figures: the latter comprised component frequencies that vary over time like natural speech. 159 participants (aged 18-79) with a range of peripheral hearing sensitivity were studied. We used hierarchal linear regression and structural equation modelling to examine how well speech-in-noise ability (for words and sentences) could be predicted by age, peripheral hearing, and static and dynamic figure-ground. Regression demonstrated that in addition to the audiogram and age, the low-frequency dynamic figure-ground accounted for significant variance of speech-in-noise, higher than the static figure-ground. The structural models showed that a combination of all types of figure-ground tasks predicted speech-in-noise with a higher effect size than the audiogram or age. Age influenced word perception in noise directly but sentence perception indirectly via effects on peripheral and central hearing. Overall, this study demonstrates that dynamic figure-ground explains more variance of real-life listening than static figure-ground, and the combination of both predicts real-life listening better than hearing sensitivity or age.

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