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噪声中句子识别促进或抑制正常听力和人工耳蜗植入者掩蔽释放。

Sentence recognition in noise promoting or suppressing masking release by normal-hearing and cochlear-implant listeners.

机构信息

Department of Otolaryngology-Head and Neck Surgery, Eye and Ear Institute, The Ohio State University, Columbus, Ohio 43212, USA.

出版信息

J Acoust Soc Am. 2012 Apr;131(4):3111-9. doi: 10.1121/1.3688511.

Abstract

Normal-hearing (NH) listeners maintain robust speech understanding in modulated noise by "glimpsing" portions of speech from a partially masked waveform--a phenomenon known as masking release (MR). Cochlear implant (CI) users, however, generally lack such resiliency. In previous studies, temporal masking of speech by noise occurred randomly, obscuring to what degree MR is attributable to the temporal overlap of speech and masker. In the present study, masker conditions were constructed to either promote (+MR) or suppress (-MR) masking release by controlling the degree of temporal overlap. Sentence recognition was measured in 14 CI subjects and 22 young-adult NH subjects. Normal-hearing subjects showed large amounts of masking release in the +MR condition and a marked difference between +MR and -MR conditions. In contrast, CI subjects demonstrated less effect of MR overall, and some displayed modulation interference as reflected by poorer performance in modulated maskers. These results suggest that the poor performance of typical CI users in noise might be accounted for by factors that extend beyond peripheral masking, such as reduced segmental boundaries between syllables or words. Encouragingly, the best CI users tested here could take advantage of masker fluctuations to better segregate the speech from the background.

摘要

正常听力 (NH) 者通过“瞥见”部分部分掩蔽波形中的语音来保持对调制噪声中语音的强大理解能力——这种现象被称为掩蔽释放 (MR)。然而,人工耳蜗植入 (CI) 用户通常缺乏这种弹性。在之前的研究中,噪声对语音的时间掩蔽是随机发生的,这使得难以确定 MR 在多大程度上归因于语音和掩蔽器的时间重叠。在本研究中,通过控制时间重叠程度,构建掩蔽器条件以促进 (+MR) 或抑制 (-MR) 掩蔽释放。在 14 名 CI 受试者和 22 名年轻成人 NH 受试者中测量了句子识别。NH 受试者在+MR 条件下表现出大量的掩蔽释放,并且+MR 和-MR 条件之间存在显著差异。相比之下,CI 受试者的 MR 总体影响较小,并且一些受试者表现出调制干扰,反映为在调制掩蔽器中的表现较差。这些结果表明,典型的 CI 用户在噪声中的较差表现可能归因于超出外围掩蔽的因素,例如音节或单词之间的片段边界减少。令人鼓舞的是,这里测试的最佳 CI 用户可以利用掩蔽器波动来更好地将语音与背景分离。

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