Optimimizing Noise-based Enhancement of Speech Recognition by Cochlear Implant Patients

0085370柯林斯众所周知，人工耳蜗可以使大多数聋人恢复一定程度的功能性听力。然而，不同受试者的语音识别能力差异很大，导致这种差异的机制也鲜为人知。一个可能阻碍人工耳蜗植入受试者语音识别的因素是，电刺激的神经的同步性比通常在听觉刺激的神经中观察到的要高得多。最近，一些研究人员提出，在语音信号中添加噪声可能会降低在电刺激下观察到的神经反应的同步性，从而可能恢复更正常的反应模式。通过生成更自然的模式，有可能改善人工耳蜗植入患者的语音识别能力。我们实验室的初步理论结果表明，在周期性电信号中加入少量的加性噪声可以诱导出更多的正常神经反应模式。此外，同样来自我们实验室的初步实验结果表明，使用这种方法也可以改善语音识别。在文献中，当加性噪声处于最佳水平时，改善非线性系统中的信号传输的现象被称为随机共振(SR)。本研究的目的是探讨并优化一种基于随机共振理论的人工耳蜗术后语音处理方法。到目前为止，随机共振的研究主要集中于在非线性系统的背景下向弱信号中添加噪声。相反，这项研究将考虑驱动复杂系统以更混乱的方式做出反应的理论基础，从而更好地模拟在正常听觉系统中观察到的反应。已经设计了一系列的理论和实验室实验来解决加性噪声在电刺激下的基本作用。将使用神经对电刺激的反应的计算模型来发展理论结果，结果将在心理物理和神经生理学实验中得到验证。虽然在传统的随机共振研究中已经考虑了在“正常”声学神经刺激下优化微弱信号的加性噪声过程，但对于受电刺激的神经系统，这个问题还没有被解决。所提出的具体问题涉及产生SR现象和在听觉系统的电刺激下优化该现象。这项工作将为驱动听觉系统以更自然的、尽管是混乱的方式做出反应奠定重要的理论基础。计算机模型的构建将提高对电刺激驱动的神经反应的理解，并有助于设计新的电刺激范例，以改善严重受损的听觉系统中的语音表征。与神经生理学家的合作将确保理论预测在人体模型中通过心理物理实验和神经生理学数据得到验证。此外，这项工作的跨学科范围将为生物医学工程师的培训提供一个独特的场所。

0085370CollinsIt is well established that cochlear implants restore some level of functional hearing to most deaf individuals. However, speech recognition abilities vary widely across subjects and the mechanisms responsible for this variability are poorly understood. One factor that may impede speech recognition by cochlear implant subjects is that electrically stimulated nerves respond with a much higher level of synchrony than what is normally observed in acoustically stimulated nerves. Recently, some researchers have suggested that adding noise to a speech signal may decrease the synchronicity of the neural response observed under electrical stimulation, and thus, might restore a more normal response pattern. By generating more natural patterns, it may be possible to improve speech recognition for cochlear implant patients. Preliminary theoretical results from our lab indicate that more normal neural response patterns can be induced when small amounts of additive noise are added to periodic electrical signals. In addition, preliminary experimental results, again from our lab, indicate that speech recognition may also be improved using this approach. In the literature, the phenomenon whereby additive noise, when presented at an optimal level, improves signal transmission in nonlinear systems is known as stochastic resonance (SR). The goal of this research is to investigate and optimize a novel speech processing approach for cochlear implant patients based on the theory of stochastic resonance. To date, SR research has focused on the addition of noise to a weak signal within the context of a nonlinear systems. This research will instead consider the theoretical basis for driving a complex system to respond in a more chaotic fashion, and thus better mimic the responses observed in the normal auditory system. A series of theoretical and laboratory experiments has been designed to address the fundamental role of additive noise under electrical stimulation. A computational model of the neural response to electrical stimulation will be employed to develop the theoretical results, and results will be verified in psychophysical as well as neurophysiological experiments. Although optimizing the additive noise process for weak signals under "normal" acoustic neural stimulation has been considered in traditional SR research, this issue has not been addressed for neural systems subject to electrical stimulation. The specific questions that are proposed involve both generating a SR phenomenon and optimizing the phenomenon under electrical stimulation of the auditory system. This work will form an important theoretical basis for driving the auditory system to respond in a more natural, albeit chaotic fashion. Construction of the computer models will improve understanding of the neural response driven by electrical stimulation and assist in the design of new electrical stimulation paradigms that improve the representation of speech within the profoundly impaired auditory system. A collaboration with a neurophysiologist will ensure that the theoretical predictions are validated in a human model via psychophysical experiments and in neurophysiological data. In addition, the interdisciplinary scope of this work will provide a unique venue for the training of biomedical engineers.