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使用频谱模糊评估人工耳蜗语音感知中通道间相互作用的影响。

Using Spectral Blurring to Assess Effects of Channel Interaction on Speech-in-Noise Perception with Cochlear Implants.

机构信息

Cambridge Hearing Group, Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge, CB2 7EF, UK.

Massachusetts Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, 02114, USA.

出版信息

J Assoc Res Otolaryngol. 2020 Aug;21(4):353-371. doi: 10.1007/s10162-020-00758-z. Epub 2020 Jun 9.

Abstract

Cochlear implant (CI) listeners struggle to understand speech in background noise. Interactions between electrode channels due to current spread increase the masking of speech by noise and lead to difficulties with speech perception. Strategies that reduce channel interaction therefore have the potential to improve speech-in-noise perception by CI listeners, but previous results have been mixed. We investigated the effects of channel interaction on speech-in-noise perception and its association with spectro-temporal acuity in a listening study with 12 experienced CI users. Instead of attempting to reduce channel interaction, we introduced spectral blurring to simulate some of the effects of channel interaction by adjusting the overlap between electrode channels at the input level of the analysis filters or at the output by using several simultaneously stimulated electrodes per channel. We measured speech reception thresholds in noise as a function of the amount of blurring applied to either all 15 electrode channels or to 5 evenly spaced channels. Performance remained roughly constant as the amount of blurring applied to all channels increased up to some knee point, above which it deteriorated. This knee point differed across listeners in a way that correlated with performance on a non-speech spectro-temporal task, and is proposed here as an individual measure of channel interaction. Surprisingly, even extreme amounts of blurring applied to 5 channels did not affect performance. The effects on speech perception in noise were similar for blurring at the input and at the output of the CI. The results are in line with the assumption that experienced CI users can make use of a limited number of effective channels of information and tolerate some deviations from their everyday settings when identifying speech in the presence of a masker. Furthermore, these findings may explain the mixed results by strategies that optimized or deactivated a small number of electrodes evenly distributed along the array by showing that blurring or deactivating one-third of the electrodes did not harm speech-in-noise performance.

摘要

人工耳蜗(CI)使用者在背景噪声中理解言语存在困难。由于电流扩散导致电极通道之间的相互作用增加了噪声对言语的掩蔽,从而导致言语感知困难。因此,减少通道相互作用的策略有可能改善 CI 使用者在噪声中的言语感知,但之前的结果存在差异。我们通过一项涉及 12 名经验丰富的 CI 用户的听力研究,调查了通道相互作用对噪声中言语感知的影响及其与频谱时间锐度的关系。我们没有试图减少通道相互作用,而是通过在分析滤波器的输入级或输出级调整电极通道之间的重叠,或者通过每个通道使用多个同时刺激的电极,引入频谱模糊来模拟通道相互作用的一些影响。我们测量了噪声中言语接收阈值,作为应用于所有 15 个电极通道或 5 个均匀间隔的通道的模糊量的函数。当应用于所有通道的模糊量增加到某个拐点时,性能基本保持不变,超过该拐点后性能就会恶化。该拐点在不同听众中的差异与非言语频谱时间任务的表现相关,这里提出将其作为衡量通道相互作用的个体指标。令人惊讶的是,即使对 5 个通道应用极端的模糊量也不会影响性能。在噪声中对言语感知的影响与在 CI 的输入和输出端进行模糊处理的效果相似。这些结果与以下假设一致,即经验丰富的 CI 用户可以利用有限数量的有效信息通道,并在存在掩蔽器的情况下识别言语时容忍其日常设置的一些偏差。此外,这些发现可能通过优化或停用均匀分布在阵列上的少数电极的策略来解释导致混合结果的原因,这些策略表明模糊或停用三分之一的电极不会损害噪声中的言语感知性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0d0/7445227/26f5e20bcb53/10162_2020_758_Fig1_HTML.jpg

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