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

I-Corps：用于训练和诊断的智能语音感知反馈

• 批准号: 1738164
• 负责人: Lynne Bernstein
• 金额: $ 5万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738164&HistoricalAwards=false
• 关键词: Corps; Smart; Speech; Perception; Feedback; Corps; Smart; Speech; Perception; Feedback

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.

该I-Corps项目的更广泛的影响/商业潜力是根据语音信号和人类语音感知的模型提供技术和服务。这些模型对物理语音刺激和推动语音感知的感知表示有深刻的了解。 要探索的技术应用可能会在以下领域中脱颖而出：（1）提高听力学测试的功效； （2）改善计算机合成的声音的现实主义（3）提供语音感知培训，以更好地理解非本地人说话者或神经系统疾病的说话者； （4）通过训练具有视听性语音处理缺陷的人，例如自闭症谱系中的个体，从而增强多感官语音感知； （5）通过纤维状刺激发展感觉替代或增强； （6）提高老年人的质量？通过视觉言语感知训练的生活，以减轻他们在嘈杂背景的听觉演讲中的下降。本I-Corps项目将根据多感官语音信号和人类语音感知的模型调查技术和服务的商业应用。 在存在于说话者，物理通信渠道和/或感知者的噪声和/或扭曲的情况下，人类语音感知可能是高度错误的（例如，听力损失）。要探索的技术语音感知如何降低，以及多感觉刺激如何弥补感知错误。例如，这项技术支持使用简单的言论任务来表征语音感知困难，其中感知者重复了刚才所说的话。在正式的实验室实验中，该技术已通过改善感知错误和训练反馈之间的意外情况来改善语音感知训练结果。该技术可用于开发旨在减少感知错误潜力的媒体。