Instituto de Neurociencias de Castilla y León, Universidad de Salamanca, 37007 Salamanca, Spain.
Instituto de Investigación Biomédica, Universidad de Salamanca, 37007 Salamanca, Spain.
J Neurosci. 2020 Aug 19;40(34):6613-6623. doi: 10.1523/JNEUROSCI.0469-20.2020. Epub 2020 Jul 17.
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. 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.
人类听觉能够适应背景噪声，这一事实表明，在噪声中，与提前呈现相比，当词稍后呈现时，听者能识别出更多孤立的词。这种适应可能是因为主导噪声会（适应）改变听觉神经元的动态范围，从而改善语音频谱和时间线索的神经编码。由于神经动态范围的适应取决于刺激水平的统计数据，因此，我们在这里研究了“统计”适应对于改善噪声背景下的语音识别的重要性。我们比较了有和没有适应噪声前导的噪声掩蔽词的识别，前导的水平要么是恒定的，要么根据不同的统计分布每 50 毫秒变化一次。对 28 名听众（9 名男性）进行了适应测量，并将前导相对于无前导条件的识别提高程度作为适应的量化指标。对于恒定水平的前导，适应最大，而对于高度波动的前导，则没有发生适应，即使这两种类型的前导具有相同的平均水平，并且都激活了内侧橄榄耳蜗反射。高度波动的前导的瞬时幅度压缩产生的适应与不压缩的恒定水平前导一样多。总的来说，结果表明，语音识别中的噪声适应可能是由对最频繁声音水平的神经动态范围适应介导的。此外，它们表明，听觉外周压缩本身，而不是内侧橄榄耳蜗反射，通过减少噪声中的水平波动，可以促进噪声适应。在噪声中识别语音具有挑战性，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，我们报告了适应对语音识别的一些益处，并表明：（1）对于具有相同平均水平的固定但非高度波动的前导，会发生适应；（2）固定和高度波动的噪声都会激活内侧橄榄耳蜗反射；（3）即使对于高度波动的前导，当刺激通过快速幅度压缩器时，也会发生适应。这些发现表明，噪声适应反映了对最频繁噪声水平的神经动态范围适应，而听觉外周压缩，而不是内侧橄榄耳蜗反射，可能促进噪声适应。
