Andéol Guillaume, Paraouty Nihaad, Giraudet Fabrice, Wallaert Nicolas, Isnard Vincent, Moulin Annie, Suied Clara
Institut de Recherche Biomédicale des Armées, 1 Place Valérie André, 91220 Brétigny sur Orge, France.
iAudiogram-My Medical Assistant SAS, 51100 Reims, France.
Biology (Basel). 2024 Jun 5;13(6):416. doi: 10.3390/biology13060416.
Understanding speech in noise is particularly difficult for individuals occupationally exposed to noise due to a mix of noise-induced auditory lesions and the energetic masking of speech signals. For years, the monitoring of conventional audiometric thresholds has been the usual method to check and preserve auditory function. Recently, suprathreshold deficits, notably, difficulties in understanding speech in noise, has pointed out the need for new monitoring tools. The present study aims to identify the most important variables that predict speech in noise understanding in order to suggest a new method of hearing status monitoring. Physiological (distortion products of otoacoustic emissions, electrocochleography) and behavioral (amplitude and frequency modulation detection thresholds, conventional and extended high-frequency audiometric thresholds) variables were collected in a population of individuals presenting a relatively homogeneous occupational noise exposure. Those variables were used as predictors in a statistical model (random forest) to predict the scores of three different speech-in-noise tests and a self-report of speech-in-noise ability. The extended high-frequency threshold appears to be the best predictor and therefore an interesting candidate for a new way of monitoring noise-exposed professionals.
对于因噪声引起的听觉损伤和语音信号的能量掩蔽混合作用而职业性暴露于噪声中的个体来说,在噪声环境中理解语音尤为困难。多年来,监测传统听力阈值一直是检查和保护听觉功能的常用方法。最近,阈上缺陷,尤其是在噪声中理解语音的困难,指出了需要新的监测工具。本研究旨在确定预测噪声中语音理解的最重要变量,以便提出一种新的听力状况监测方法。在一组职业噪声暴露相对均匀的个体中收集了生理变量(耳声发射的畸变产物、耳蜗电图)和行为变量(幅度和频率调制检测阈值、传统和扩展高频听力阈值)。这些变量被用作统计模型(随机森林)中的预测因子,以预测三种不同的噪声中语音测试的分数以及噪声中语音能力的自我报告。扩展高频阈值似乎是最佳预测因子,因此是监测噪声暴露专业人员新方法的一个有趣候选因素。