Davies-Venn Evelyn, Nelson Peggy, Souza Pamela
Department of Speech-Language-Hearing Sciences, University of Minnesota, 164 Pillsbury Drive Southeast, Minneapolis, Minnesota 55455, USA.
Department of Communication Sciences and Disorders and Knowles Hearing Center, Northwestern University, 2240 Campus Drive, Evanston, Illinois 60208, USA.
J Acoust Soc Am. 2015 Jul;138(1):492-503. doi: 10.1121/1.4922700.
Some listeners with hearing loss show poor speech recognition scores in spite of using amplification that optimizes audibility. Beyond audibility, studies have suggested that suprathreshold abilities such as spectral and temporal processing may explain differences in amplified speech recognition scores. A variety of different methods has been used to measure spectral processing. However, the relationship between spectral processing and speech recognition is still inconclusive. This study evaluated the relationship between spectral processing and speech recognition in listeners with normal hearing and with hearing loss. Narrowband spectral resolution was assessed using auditory filter bandwidths estimated from simultaneous notched-noise masking. Broadband spectral processing was measured using the spectral ripple discrimination (SRD) task and the spectral ripple depth detection (SMD) task. Three different measures were used to assess unamplified and amplified speech recognition in quiet and noise. Stepwise multiple linear regression revealed that SMD at 2.0 cycles per octave (cpo) significantly predicted speech scores for amplified and unamplified speech in quiet and noise. Commonality analyses revealed that SMD at 2.0 cpo combined with SRD and equivalent rectangular bandwidth measures to explain most of the variance captured by the regression model. Results suggest that SMD and SRD may be promising clinical tools for diagnostic evaluation and predicting amplification outcomes.
一些听力损失患者尽管使用了能优化可听度的放大装置,但言语识别得分仍很低。除了可听度之外,研究表明,诸如频谱和时间处理等阈上能力可能解释了放大言语识别得分的差异。已经使用了各种不同方法来测量频谱处理。然而,频谱处理与言语识别之间的关系仍然没有定论。本研究评估了听力正常和听力损失患者的频谱处理与言语识别之间的关系。使用从同步带凹口噪声掩蔽估计的听觉滤波器带宽来评估窄带频谱分辨率。使用频谱纹波辨别(SRD)任务和频谱纹波深度检测(SMD)任务来测量宽带频谱处理。使用三种不同的测量方法来评估安静和噪声环境下未放大和放大后的言语识别。逐步多元线性回归显示,每倍频程2.0周期(cpo)的SMD能显著预测安静和噪声环境下放大和未放大言语的言语得分。共性分析显示,每倍频程2.0周期的SMD与SRD及等效矩形带宽测量相结合,可解释回归模型所捕获的大部分方差。结果表明,SMD和SRD可能是用于诊断评估和预测放大效果的很有前景的临床工具。