Department of Computer Science, University of Sheffield, Sheffield S1 4DP, United Kingdom.
J Acoust Soc Am. 2010 Feb;127(2):943-54. doi: 10.1121/1.3273893.
The neural mechanisms underlying the ability of human listeners to recognize speech in the presence of background noise are still imperfectly understood. However, there is mounting evidence that the medial olivocochlear system plays an important role, via efferents that exert a suppressive effect on the response of the basilar membrane. The current paper presents a computer modeling study that investigates the possible role of this activity on speech intelligibility in noise. A model of auditory efferent processing [Ferry, R. T., and Meddis, R. (2007). J. Acoust. Soc. Am. 122, 3519-3526] is used to provide acoustic features for a statistical automatic speech recognition system, thus allowing the effects of efferent activity on speech intelligibility to be quantified. Performance of the "basic" model (without efferent activity) on a connected digit recognition task is good when the speech is uncorrupted by noise but falls when noise is present. However, recognition performance is much improved when efferent activity is applied. Furthermore, optimal performance is obtained when the amount of efferent activity is proportional to the noise level. The results obtained are consistent with the suggestion that efferent suppression causes a "release from adaptation" in the auditory-nerve response to noisy speech, which enhances its intelligibility.
人类听众在背景噪声中识别语音的能力的神经机制仍未被完全理解。然而，越来越多的证据表明，内侧橄榄耳蜗系统通过对基底膜反应产生抑制作用的传出神经发挥着重要作用。本文提出了一项计算机建模研究，旨在探讨这种活动对噪声中语音可懂度的可能作用。使用听觉传出处理模型[Ferry，R. T.，和 Meddis，R.（2007）。J. Acoust. Soc. Am. 122，3519-3526]为统计自动语音识别系统提供声学特征，从而可以量化传出活动对语音可懂度的影响。在语音不受噪声干扰的情况下，“基本”模型（无传出活动）在连接数字识别任务中的性能良好，但在存在噪声时性能下降。然而，当应用传出活动时，识别性能会大大提高。此外，当传出活动的量与噪声水平成正比时，可获得最佳性能。所得结果与传出抑制导致听觉神经对噪声语音的反应“适应释放”的观点一致，从而提高了其可懂度。
