Human hearing adapts to background noise, as evidenced by the fact that listeners recognize more isolated words when words are presented later rather than earlier in noise. This adaptation likely occurs because the leading noise shifts ("adapts") the dynamic range of auditory neurons, which can improve the neural encoding of speech spectral and temporal cues. Because neural dynamic range adaptation depends on stimulus-level statistics, here we investigated the importance of "statistical" adaptation for improving speech recognition in noisy backgrounds. We compared the recognition of noised-masked words in the presence and in the absence of adapting noise precursors whose level was either constant or was changing every 50 ms according to different statistical distributions. Adaptation was measured for 28 listeners (9 men) and was quantified as the recognition improvement in the precursor relative to the no-precursor condition. Adaptation was largest for constant-level precursors and did not occur for highly fluctuating precursors, even when the two types of precursors had the same mean level and both activated the medial olivocochlear reflex. Instantaneous amplitude compression of the highly fluctuating precursor produced as much adaptation as the constant-level precursor did without compression. Together, results suggest that noise adaptation in speech recognition is probably mediated by neural dynamic range adaptation to the most frequent sound level. Further, they suggest that auditory peripheral compression per se, rather than the medial olivocochlear reflex, could facilitate noise adaptation by reducing the level fluctuations in the noise.

SIGNIFICANCE STATEMENT Recognizing speech in noise is challenging but can be facilitated by noise adaptation. The neural mechanisms underlying this adaptation remain unclear. Here, we report some benefits of adaptation for word-in-noise recognition and show that (1) adaptation occurs for stationary but not for highly fluctuating precursors with equal mean level; (2) both stationary and highly fluctuating noises activate the medial olivocochlear reflex; and (3) adaptation occurs even for highly fluctuating precursors when the stimuli are passed through a fast amplitude compressor. These findings suggest that noise adaptation reflects neural dynamic range adaptation to the most frequent noise level and that auditory peripheral compression, rather than the medial olivocochlear reflex, could facilitate noise adaptation.

人类的听力适应了背景噪音，这一事实证明了听众在较晚而不是较早出现噪音时会听到更多孤立的单词。由于前导噪声会移动（“适应”）听觉神经元的动态范围，因此可能会发生这种适应，这可以改善语音频谱和时间提示的神经编码。由于神经动态范围的适应性取决于刺激水平的统计数据，因此在这里我们研究了“统计性”适应性对改善嘈杂背景下语音识别的重要性。我们比较了在存在和不存在噪声前兆的情况下，对噪声掩盖的单词的识别，噪声前兆的水平是恒定的，或者是根据不同的统计分布每50 ms更改一次。测量了28位听众（9位男性）的适应能力，并将其量化为相对于无先驱者状况，前体的认知改善。对于恒定水平的前体，适应是最大的，而对于波动较大的前体，则不会发生，即使两种类型的前体具有相同的平均水平并且都激活了内侧小腱反射。高波动前体的瞬时振幅压缩产生的变化与恒定水平前体在没有压缩的情况下产生的适应性一样大。总之，结果表明，语音识别中的噪声适应可能是由神经动态范围适应最频繁的声音水平所介导的。此外，他们建议听觉外周压迫本身，而不是内侧小腱膜反射，

意义声明在噪声中识别语音是一项挑战，但可以通过噪声适应来促进。这种适应的神经机制尚不清楚。在这里，我们报告了适应对噪声中的单词识别的一些好处，并表明（1）适应发生在平稳的情况下，而不发生在平均水平相等的高波动前体上；（2）平稳的和剧烈波动的噪音都激活了内侧小腱反射；（3）当刺激物通过快速振幅压缩器时，即使对于高度波动的前体，也会发生适应。这些发现表明，噪声适应反映了神经动态范围对最常见噪声水平的适应，并且听觉外周压迫而不是内侧小腱膜反射可以促进噪声适应。