Auditory Processing of Complex Sounds

描述（由申请人提供）：从公共卫生的角度来看，理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中，我们正在研究对区分语音至关重要的声学参数的神经反应。这个建议的重点是在安静和噪音的元音神经编码。重点研究元音的基本原理是它们在传递信息，特别是在话语中的基本作用，以及它们在所有已知语音系统中的中心地位。我们已经开发了一种新的，可测试的假设，在中脑的两个显着特点的元音：基频（F0），或语音音高，共振峰频率，频谱峰，区分元音的强大代表。这一假设考虑到以下事实：i）除了具有最佳频率（BF）之外，大多数中脑神经元针对音高范围内的周期性进行调谐，以及ii）外围响应中的周期性的强度根据BF和共振峰频率之间的关系而系统地改变。特别是，频率波动的神经（AN）的反应，是同步到一个元音的F0是弱的共振峰频率附近调谐的纤维和共振峰之间调谐的纤维强。这种低频速率波动幅度的变化跨越AN传播到中脑，在那里神经元对调制频率敏感，根据BF和元音共振峰频率之间的关系，有很大的速率变化。跨中脑神经元的速率分布对元音的共振峰频率进行编码，并且在宽范围的声级和存在噪声的情况下是鲁棒的。这种代码是适当的外周调谐的变化，减少外周非线性的强度，如同步捕获，并与老化相关的中央抑制处理的变化。我们的元音编码假设将通过定量相关的行为阈值检测和共振峰的生理反应在中脑水平的歧视进行测试。我们将进一步发展我们的模型，在听觉中脑的信号处理，包括神经处理的非线性特征，锁模，这是在中脑观察。我们假设，锁模有助于代表强周期性的声音，如有声语音，通过提高神经元的响应与带通调制调谐到强调制的声音。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的神经表征的经典模型有着根本的不同，因此恢复它的信号处理策略也将与现有的策略有着根本的不同。

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.