Abavisani Ali, Allen Jont B
Department of Electrical and Computer Engineering, The Beckman Institute, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, Illinois 61801, USA.
J Acoust Soc Am. 2017 Dec;142(6):3736. doi: 10.1121/1.5016852.
The goal of this study is to provide a metric for evaluating a given hearing-aid insertion gain using a consonant recognition based measure. The basic question addressed is how treatment impacts phone recognition at the token level, relative to a flat insertion gain, at the most-comfortable-level (MCL). These tests are directed at fine-tuning a treatment, with the ultimate goal of improving speech perception, and to identify when a hearing level gain-based treatment degrades phone recognition. Eight subjects with hearing loss were tested under two conditions: flat-gain and a treatment insertion gain, based on subject's hearing level. The speech corpus consisted of consonant-vowel tokens at different signal to speech-weighted noise conditions, presented at the subject's MCL. The treatment caused the average score to improve for 31% of the trials and decrease for 12%. An analysis method based on the accumulated error differences was devised to quantify the benefit each individual ear received from the treatment. Using this measure, the effect of the treatment could be evaluated, providing precise characterization of idiosyncratic phone recognition. This analysis directs the audiologist toward the most susceptible subject-dependent tokens, to focus in the process of fine-tuning the insertion gain of the hearing-aid.
本研究的目标是提供一种度量标准,用于使用基于辅音识别的方法评估给定助听器的插入增益。所解决的基本问题是,相对于在最舒适响度级(MCL)下的平坦插入增益,治疗如何在音素水平上影响音素识别。这些测试旨在对治疗进行微调,最终目标是改善言语感知,并确定基于听力级增益的治疗何时会降低音素识别。八名听力损失受试者在两种条件下接受测试:基于受试者听力水平的平坦增益和治疗插入增益。语音语料库由不同信号与语音加权噪声条件下的辅音 - 元音音素组成,在受试者的MCL下呈现。治疗使31%的试验平均得分提高,12%的试验平均得分降低。设计了一种基于累积误差差异的分析方法,以量化每只耳朵从治疗中获得的益处。使用这种度量标准,可以评估治疗效果,提供对个体音素识别特性的精确表征。这种分析指导听力学家关注最易受影响的个体受试者音素,以便在微调助听器插入增益的过程中集中精力。