PDF(Pubmed)
Abstract：
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.
摘要：
由于噪声引起的听觉损伤和语音信号的能量掩蔽的混合，对于职业上暴露于噪声的个人来说，理解噪声中的语音特别困难。多年来,常规测听阈值的监测一直是检查和保持听觉功能的常用方法。最近,超越赤字，特别是,在噪音中理解语音的困难，指出需要新的监测工具。本研究旨在确定在噪声理解中预测语音的最重要变量，以提出一种新的听力状态监测方法。生理(耳声发射的畸变产物，耳蜗电图)和行为(振幅和频率调制检测阈值，常规和扩展的高频测听阈值）变量在具有相对均匀的职业噪声暴露的人群中收集。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语音测试的得分和噪声语音能力的自我报告。扩展的高频阈值似乎是最好的预测指标，因此是监视噪声暴露专业人员的新方法的有趣候选人。
