人工耳蜗植入
听力学
言语感知
感知
分辨率(逻辑)
心理学
人工耳蜗植入术
声学
计算机科学
医学
物理
人工智能
神经科学
作者
Samin Ashjaei,Reed Farrar,Madison Paxton,Kathryn L. Morgan,Meisam K. Arjmandi,Meisam Arjmandi
标识
DOI:10.1101/2025.04.28.25326599
摘要
Abstract Purpose Reduced spectral resolution limits speech recognition in cochlear implant (CI) listeners. Although many studies have examined this association, uncertainties remain regarding its strength and contributing methodological and clinical factors. This narrative review synthesizes findings from studies of postlingually deafened adult CI listeners, focusing on psychophysical measures of spectral resolution and their strengths and limitations. Method We reviewed studies published through January 2025 that examined the relationship between psychophysical measures of spectral resolution and speech perception outcomes in postlingually deafened adult CI listeners. Twenty-four studies met inclusion criteria and tested this association statistically. Where available, the coefficient of determination ( R² ) was extracted to quantify the variance in speech recognition outcomes explained by spectral resolution measures. Results Several studies found a statistically significant association between psychophysical measures of spectral resolution and speech recognition performance. The strength of this association varied widely ( R² = 0.21 to 0.68), depending on the spectral resolution measure and the speech material used. Variability in R² values reflects differences in test procedures, study populations, and speech materials. Conclusions Several psychophysical measures of spectral resolution are promising predictors of speech recognition and may serve as valuable tools for evaluating new CI signal processing algorithms, programming strategies, and auditory rehabilitation. A deeper understanding of the spectral resolution–speech perception relationship requires examining the distinct contributions of both peripheral and central auditory processes. Variability in observed associations highlights the need for further mechanistic research into the pathways linking spectral resolution to speech recognition outcomes.
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