Auditory Processing of Complex Sounds

描述(申请人提供)：从公共健康的角度来看，了解语音的处理对于显著改善人们的生活至关重要。 患有沟通障碍。由于内耳和中枢听觉神经元的强烈非线性，日常经历的声音和噪声水平范围内的语音神经代码是难以捉摸的。在与语音学家的合作下，我们正在研究神经对声学参数的反应，这些参数对区分语音至关重要。本研究的重点是安静和噪声环境下元音的神经编码。关注元音的基本原理是元音在传递信息方面的基本作用，尤其是在语篇中，以及元音在所有已知言语系统中的中心地位。我们提出了一个新的、可检验的假说，说明元音的两个显著特征在中脑中的强健表现：基频(F0)，或语调，以及共振峰频率，即区分元音的光谱峰值。这一假设考虑了这样一个事实：i)除了有一个最佳频率(BF)外，大多数中脑神经元都是在音高范围内进行周期性调谐的，以及ii)外周TE反应的周期性的强度取决于BF和共振峰频率之间的关系。特别是，与元音F0同步的听神经(AN)反应的速率波动对于调谐在共振峰频率附近的纤维来说是微弱的，而对于调谐在共振峰之间的纤维来说是强烈的。这种低频频率波动幅度的变化通过AN传播到中脑，在那里对调制频率敏感的神经元有很大的速率变化，这取决于BF和元音共振峰频率之间的关系。整个中脑神经元的频率分布编码了元音的共振峰频率，在广泛的音级和存在噪音的情况下都是健壮的。这种代码很容易受到外周调谐的变化、外周非线性强度的降低(如同步捕获)以及与衰老相关的中枢抑制处理的变化的影响。我们的元音编码假说将通过在中脑水平上将检测和辨别共振峰的行为阈值与生理反应定量地联系起来来检验。我们将进一步开发我们的听觉中脑信号处理模型，以包括在中脑中观察到的神经处理的非线性特征-模式锁定。我们假设，模式锁定有助于强周期性声音的表示，如浊音，通过带通调制调谐增强神经元对强调制声音的反应。这项工作将导致改进的信号处理算法的开发，以帮助越来越多的听力损失患者。由于我们的元音编码假说提出的表征与语音神经表征的经典模型有根本的不同，恢复它的信号处理策略将与现有的策略有根本的不同。

DESCRIPTION (provided by applicant): From a public health perspective, understanding the processing of speech sounds is critical in effecting significant improvement in the lives of people with communication disorders. The neural code for speech over the range of sound and noise levels experienced daily is elusive due to strong nonlinearities of the inner ear and central auditory neurons. In collaboration with a phonetician, we are studying neural responses to acoustic parameters that are crucial to differentiating speech sounds. This proposal focuses on the neural coding of vowels in quiet and in noise. The rationale for focusing on vowels is their fundamental role in carrying information, especially in discourse, and their centrality in all know speech systems. We have developed a novel, testable hypothesis for the robust representation in the midbrain of two salient features of vowels: fundamental frequency (F0), or voice pitch, and formant frequencies, the spectral peaks that differentiate vowels. This hypothesis takes into account the facts that i) in addition to having a best frequency (BF), most midbrain neurons are tuned for periodicities in the range of voice pitch, and ii) the strength of the periodicities in te response of the periphery changes systematically depending upon the relation between BF and formant frequency. In particular, the rate fluctuations of auditory-nerve (AN) responses that are synchronized to the F0 of a vowel are weak for fibers tuned near formant frequencies and strong for fibers tuned between formants. This variation in the amplitude of low-frequency rate fluctuations across the AN is propagated to the midbrain, where neurons sensitive to modulation frequency have large rate changes depending on the relation between BF and vowel formant frequencies. The profile of rates across midbrain neurons encodes the formant frequencies of vowels and is robust across a wide range of sound levels and in the presence of noise. This code is appropriately vulnerable to changes in peripheral tuning, decreases in the strength of peripheral nonlinearities such as synchrony capture, and to changes in central inhibitory processing associated with aging. Our vowel-coding hypothesis will be tested by quantitatively relating behavioral thresholds for detection and discrimination of formants to physiological responses at the level of the midbrain. We will further develop our models for signal processing in the auditory midbrain to include a nonlinear feature of neural processing, mode-locking, that is observed in the midbrain. We hypothesize that mode-locking contributes to the representation of strongly periodic sounds, such as voiced speech, by boosting the response of neurons with band-pass modulation tuning to strongly modulated sounds. This work will lead to the development of improved signal-processing algorithms to assist the growing number of people who are afflicted with hearing loss. Because the representation proposed by our vowel-coding hypothesis is fundamentally different from classical models for neural representations of speech sounds, the signal-processing strategies to restore it will differ fundamentally from existing strategies.