I-Corps: Smart Speech Perception Feedback for Training and Diagnostics

I-Corps项目更广泛的影响/商业潜力是提供基于语音信号和人类语音感知模型的技术和服务。这些模型结合了对物理语音刺激和驱动语音感知的感知表征的深刻理解。有待探索的技术应用可以在以下领域开辟新的领域：(1)提高听力学测试的效率；(2)提高计算机合成声音的真实感(3)提供语音感知训练，以便更好地理解非母语人士或有神经障碍的人士；(4)通过对自闭症等有视听语音处理缺陷的个体进行训练，增强多感觉语音感知能力；(5)通过振动触觉刺激发展感官替代或增强；(6)提高老年人的素质。通过视觉语言感知训练来改善他们在嘈杂背景下听觉语言感知能力的下降。这个I-Corps项目将研究基于多感官语音信号和人类语音感知模型的技术和服务的商业应用。当存在来自说话者、物理通信通道和/或感知者（例如听力损失）的噪音和/或扭曲时，人类的语音感知可能会出现高度错误。要探索的技术建模如何语音感知退化，以及多感官刺激如何补偿感知错误。例如，这项技术支持通过一个简单的对讲任务来描述语音感知困难，在这个任务中，感知者重复刚刚说过的话。在正式的实验室实验中，该技术已被用于通过改善感知错误和训练反馈之间的偶然性来改善语音感知训练结果。这项技术可用于开发旨在减少感知错误可能性的媒体。

The broader impact/commercial potential of this I-Corps project is to provide technologies and services based on models of speech signals and human speech perception. These models incorporate a deep understanding of the physical speech stimulus and the perceptual representations that drive speech perception. Technology applications to be explored can break new ground in areas such as: (1) Improving the efficacy of audiological testing; (2) Improving the realism of computer-synthesized voices (3) Providing speech perception training for better understanding of non-native speakers or speakers with neurological disorders; (4) Enhancing multisensory speech perception through training of individuals with audiovisual speech processing deficits such as individuals on the autism spectrum; (5) Developing sensory substitution or augmentation through vibrotactile stimuli; and (6) Improving the quality of older adults? lives through visual speech perception training to ameliorate their declines in perceiving auditory speech in noisy backgrounds.This I-Corps project will investigate commercial applications of technologies and services based on models of multisensory speech signals and human speech perception. Human speech perception can be highly errorful in the presence of noise and/or distortions that originate in the talker, the physical communication channel, and/or the perceiver (e.g., hearing loss). The technology to be explored models how speech perception degrades, and how multisensory stimuli compensate for perceptual errors. For example, this technology supports the characterization of speech perception difficulties using a simple talk-back task in which the perceiver repeats what was just said. In formal laboratory experiments, this technology has been used to improve speech perception training outcomes through improvements in the contingencies between perceptual errors and training feedback. This technology can be used in developing media that are designed to reduce the potential for perceptual errors.