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

优化人工耳蜗植入患者基于噪声的语音识别增强功能

• 批准号: 0085370
• 负责人: Leslie Collins
• 金额: $ 28.34万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-11-01 至 2003-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0085370&HistoricalAwards=false
• 关键词: Optimimizing; Noise; based; Enhancement; Speech

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.

0085370Collinsit已良好确定，人工耳蜗植入对大多数聋人的恢复了一定程度的功能听力。但是，语音识别能力在各个受试者之间差异很大，并且对这种可变性的机制知之甚少。 可能阻碍人工耳蜗受试者语音识别的一个因素是，电刺激的神经的同步水平要高于声学刺激的神经中通常观察到的同步水平要高得多。 最近，一些研究人员提出，向语音信号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，因此可能会恢复更正常的响应模式。 通过产生更多的自然模式，可以改善人工耳蜗患者的语音识别。我们实验室的初步理论结果表明，当将少量的添加噪声添加到周期性电信号中时，可以引起更正常的神经反应模式。 此外，初步实验结果再次来自我们的实验室，表明使用这种方法也可以改善语音识别。 在文献中，在最佳水平以最佳水平呈现添加剂的现象中，非线性系统中的信号传递称为随机共振（SR）。这项研究的目的是根据随机共振理论调查和优化针对人工耳蜗患者的新型语音处理方法。迄今为止，SR研究集中在非线性系统的背景下将噪声添加到弱信号中。 相反，这项研究将考虑推动复杂系统以更混乱的方式响应的理论基础，从而更好地模仿正常听觉系统中观察到的反应。 一系列理论和实验室实验旨在解决电刺激下加性噪声的基本作用。 将采用神经对电刺激的神经反应的计算模型来发展理论结果，并将在心理物理和神经生理实验中验证结果。 尽管在传统的SR研究中考虑了在“正常”声神经刺激下为弱信号优化弱信号的添加噪声过程，但尚未针对受到电刺激的神经系统解决此问题。 提出的特定问题既涉及产生SR现象，又要在听觉系统的电刺激下优化现象。这项工作将构成一个重要的理论基础，以推动听觉系统以更自然的，尽管是混乱的方式做出反应。 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式，从而改善了深远的听觉系统中语音的表示。 与神经生理学家的合作将确保通过心理生理实验和神经生理数据在人类模型中验证理论预测。 此外，这项工作的跨学科范围将为培训生物医学工程师提供独特的场所。