Neurophysiological Mechanisms of Speech Intelligibility in Noise - A Quantitative Framework

项目摘要/摘要 在有许多声源的嘈杂环境中进行通信对听觉提出了巨大的要求 系统。在这样的场景中成功地提取目标语音信息需要结合精确的 声音信息的耳蜗转导和神经编码，以及有效的下游认知过程 其使用编码信息来分离和选择性地处理感兴趣的目标语音。演讲中- 因此，噪音问题(例如，在老年或听力丧失时)可由受损的自下而上编码引起 信息，或认知能力的下降。尽管这两个组件的存在很好地 认识到，目前还没有一个综合框架来量化每一项对 在噪声中的语音可理解性，并用于识别正在经历的个人的哪一方面存在缺陷 听力有问题。这项提案的具体目标旨在通过衡量 包膜信息的神经生理学表征，并研究它如何随两个“底部”变化。 同一个体的“向上”操纵和“自上而下”操纵。首先，信封编码在 将使用脑电(EEG)直接测量脑干和皮质。脑电指标将会 然后通过检查它们在语音呈现时如何共同变化来与感知可理解性相关联 不同水平和类型的背景噪音，以及不同个体之间的差异(目标1)。接下来，对于 同样的个体，注意集中在与目标1相同的信封编码措施上的影响将是 通过保持输入语音混合恒定并操纵哪个语音源是 选择性注意(目标2)。这种方法有助于隔离自上而下的组件。重要的是，进行所有 同一个体受试者的可理解性和脑电测量使我们能够利用个体 差异并使用回归技术来表征自下而上和自上而下的相对贡献 性能的机制。最后，对于目标1中使用的语音-噪声混合，我们将计算相同的值 在计算听神经模型的输出端对指标进行包络编码(目标3)。的关键机制 耳蜗功能障碍将被纳入到模型中，以表征它们对包络编码的影响 在嘈杂声中讲话。通过比较“模型”(目标3)和“神经”指标(目标1和2)，我们将测试 耳蜗机制可以解释自下而上编码中的个体差异。所获得的知识 通过拟议的一组实验将是发展客观诊断学和 针对个别患者的噪声中语音问题的特定性质而量身定做的干预措施 亲身体验。项目完成还将为申请人提供计算建模方面的培训， 心理物理和脑电实验设计、数据收集、分析和解释，以及科学 假设检验。这补充了她在信号处理和统计方面的现有背景，并将设置 她走上了一条坚实的道路，朝着她在听觉神经科学方面的学术研究生涯的长期目标前进。

Project Summary/Abstract Communicating in noisy environments with many sound sources places enormous demands on the auditory system. Successfully extracting target speech information in such scenarios requires a combination of precise cochlear transduction and neural coding of sound information, and effective downstream cognitive processes that use the encoded information to segregate and selectively process the target speech of interest. Speech-in- noise problems (e.g., in old age or hearing loss) can thus arise from impaired “bottom-up” coding of information, or from declines in cognitive ability. Although the existence of these two components is well recognized, there is currently no integrative framework for quantifying the relative contributions of each to speech intelligibility in noise, and for identifying which aspect is deficient in an individual who is experiencing listening problems. The specific aims of this proposal are designed to address this gap by measuring the neurophysiological representation of envelope information and investigating how that varies with both “bottom- up” manipulations and “top-down” manipulations in the same individuals. First, envelope coding in the brainstem and cortex will be directly measured using electroencephalography (EEG). The EEG metrics will then be linked to perceptual intelligibility by examining how they covary as the speech is presented with different levels and types of background noise, and how they covary across individuals (Aim 1). Next, for the same individuals, the effect of attentional focus on the same envelope coding measures as Aim 1 will be examined by keeping the input speech mixture constant and manipulating which speech source is the focus of selective attention (Aim 2). This approach helps isolate the top-down component. Importantly, conducting all intelligibility and EEG measurements in the same individual subjects allows us to leverage individual differences and use regression techniques to characterize the relative contributions of bottom-up and top-down mechanisms to performance. Finally, for the speech-noise mixtures used in Aim 1, we will calculate the same envelope coding metrics at the output of a computational auditory-nerve model (Aim 3). Key mechanisms of cochlear dysfunction will be incorporated into the model to characterize their effects on envelope coding of speech in noise. By comparing “model” (Aim 3) and “neural” metrics (Aims 1 and 2), we will test whether cochlear mechanisms can account for the individual differences in bottom-up coding. The knowledge gained through the proposed set of experiments will be foundational in the development of objective diagnostics and interventions tailored for the specific nature of speech-in-noise problems that an individual patient is experiencing. Project completion will also provide the applicant with training in computational modeling, psychophysical and EEG experiment design, data collection, analysis, and interpretation, as well as scientific hypothesis testing. This complements her existing background in signal processing and statistics, and will set her on a solid path towards her long-term goal of an academic research career in auditory neuroscience.