Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, USA.
Research Laboratory, MED-EL Corporation, Raleigh-Durham, NC, USA.
Int J Audiol. 2021 Nov;60(11):849-857. doi: 10.1080/14992027.2021.1893839. Epub 2021 Mar 15.
The primary purpose of this project was to evaluate the influence of speech audibility on speech recognition with frequency composition, a frequency-lowering algorithm used in hearing aids.
Participants were tested to determine word and sentence recognition thresholds in background noise, with and without frequency composition. The audibility of speech was quantified using the speech intelligibility index (SII).
Participants included 17 children (ages 6-16) and 21 adults (ages 19 to 72) with bilateral mild-to-severe sensorineural hearing loss.
Word and sentence recognition thresholds did not change significantly with frequency composition. Participants with better aided speech audibility had better speech recognition in noise, regardless of processing condition, than those with poorer aided audibility. For the child participants, changes in the word recognition threshold between processing conditions were predictable from aided speech audibility. However, this relationship depended strongly on one participant with a low SII and otherwise, changes in speech recognition between frequency composition off and on were not predicable from aided speech audibility.
While these results suggest that children who have a low-aided SII may benefit from frequency composition, further data are needed to generalise these findings to a greater number of participants and variety of stimuli.
本项目的主要目的是评估在助听器中使用的一种频率降低算法——频率组成对语音识别的影响，即语音可懂度对语音识别的影响。
本研究通过测试参与者在背景噪声下识别单词和句子的阈值，评估有无频率组成的情况。使用言语可懂度指数（SII）来量化言语的可懂度。
参与者包括 17 名患有双侧轻到重度感音神经性听力损失的儿童（6-16 岁）和 21 名成年人（19-72 岁）。
频率组成对单词和句子识别阈值没有显著影响。在处理条件下，助听语音可懂度较好的参与者在噪声中的语音识别能力优于助听语音可懂度较差的参与者。对于儿童参与者，处理条件之间的单词识别阈值变化可从助听语音可懂度来预测。然而，这种关系强烈依赖于一个 SII 较低的参与者，否则，在频率组成关闭和打开之间的语音识别变化不能从助听语音可懂度来预测。
虽然这些结果表明助听 SII 较低的儿童可能受益于频率组成，但需要更多的数据来将这些发现推广到更多的参与者和更多样化的刺激。
