Auditory Processing of Complex Sounds

在噪声中理解语音的挑战，特别是对感音神经性听力损失的听者 (SNHL)，不能用传统的神经编码模型来解释。这些模型通常基于 平均放电率和(或)对听神经(AN)纤维颞叶细微结构的锁相。因为 这些响应特性仅受轻度到中度SNHL的微弱影响，这样的模型无法解释 相对少量的听力损失造成的严重困难。我们最近对以下问题的反应研究 兔子和虎皮鹦鹉的中脑将我们的注意力引导到了显然可以解决 这个问题。众所周知，中脑神经元受到低频的强烈驱动(或抑制)。 幅度调制(AM)。我们最近观察到，中脑神经元对 频率啁啾可以匹配或超过其AM敏感度。神经的波幅和频率的变化 外围频率通道的响应产生了强烈的波动对比，编码了 浊音中的谱峰(共振峰)。我们假设中脑对神经幅度的敏感度 外周响应中的频率波动提供了复杂的 声音，包括语音。Aim 1通过对中脑的生理和行为研究来验证这一假设 对组合了这些线索的刺激的反应，包括带有冲突线索的“设计者”刺激以确定 它们可能会如何相互作用。这些结果将被用来测试和改进一个新的中脑计算模型 对这些线索敏感的反应。Aim 2用中脑的生理反应检验假说 神经元到有声语音，以基于每个神经元的特征直接测试模型预测 对这些线索的敏感性。 理解外周的幅度和频率波动在语音编码中的作用 反应在临床上意义重大，因为这些波动很容易受到SNHL的影响。在目标3中，我们将测试 可以在合成语音中操纵幅度和频率波动的假设 在安静和嘈杂环境中，无论有无SNHL，都会影响人类听者的可理解性。健康的人 听觉系统，波动对比被映射到强健的中脑反应，在那里，频率和定时提示 (即，对F0相关波动的锁相)在大范围的声级和噪声中持续存在。 因为波动的对比度取决于内毛细胞的饱和度，而内毛细胞又受耳蜗的影响 放大，内毛细胞或外毛细胞敏感度的病理降低降低了波动对比 在外围，最终在中脑。基于模型的算法将操纵波动对比 提高(或降低)听众的清晰度。建模、生理学和 行为研究支持提出的假设。最终，我们的目标是将这种方法扩展到 操控跑步语音中的波动对比，有效地为受损的耳朵“纠正”声音。

The challenge of understanding speech in noise, especially for listeners with sensorineural hearing loss (SNHL), cannot be explained by conventional models of neural coding. These models are typically based on average discharge rates and/or phase-locking to temporal fine structure of auditory-nerve (AN) fibers. Because these response properties are only weakly affected by mild to moderate SNHL, such models fail to explain the significant difficulties caused by relatively small amounts of hearing loss. Our recent studies of responses in the midbrain of rabbit and budgerigar have directed our attention to response features that apparently solve this problem. Midbrain neurons are well known to be strongly driven (or suppressed) by low-frequency amplitude modulations (AM). We have recently observed that selectivity of midbrain neurons to the direction of frequency chirps can match or exceed their AM sensitivity. Changes in amplitudes and frequencies of neural responses across peripheral frequency channels create strong fluctuation contrasts that encode the location of spectral peaks (formants) in voiced speech. We hypothesize that midbrain sensitivity to neural amplitude and frequency fluctuations in peripheral responses provides a robust representation of complex sounds, including speech. Aim 1 tests this hypothesis with physiological and behavioral studies of midbrain responses to stimuli that combine these cues, including “designer” stimuli with conflicting cues to determine how they may interact. These results will be used to test and refine a new computational model for midbrain responses with sensitivity to these cues. Aim 2 tests the hypothesis with physiological responses of midbrain neurons to voiced speech, to directly test model predictions based on characterization of each neuron's sensitivity to these cues. Understanding the role in speech coding of the amplitude and frequency fluctuations in peripheral responses is clinically significant because these fluctuations are vulnerable to SNHL. In Aim 3, we will test the hypothesis that amplitude and frequency fluctuations can be manipulated in synthetic speech to influence intelligibility in human listeners with or without SNHL, in quiet and in noise. In the healthy auditory system, fluctuation contrasts are mapped into a robust midbrain response, where rate and timing cues (i.e., phase-locking to the F0-related fluctuations) persist across a wide range of sound levels and in noise. Because fluctuation contrasts depend upon saturation of inner hair cells, which is in turn influenced by cochlear amplification, pathological reduction of either inner or outer hair cell sensitivity reduces the fluctuation contrasts in the periphery and ultimately in the midbrain. Model-based algorithms will manipulate the fluctuation contrasts to improve (or degrade) intelligibility in listeners. Preliminary results from modeling, physiological, and behavioral studies support the proposed hypotheses. Ultimately, our goal is to extend this approach to manipulate fluctuation contrasts in running speech, to effectively “correct” sound for the impaired ear.