EAGER: Collaborative Research: Towards Modeling Human Speech Confusions in Noise

EAGER：协作研究：对噪声中的人类语音混乱进行建模

• 批准号: 1248047
• 负责人: Nelson Morgan
• 金额: $ 10万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1248047&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Towards; Modeling

This EArly-concept Grant for Exploratory Research (EAGER) supports an exploratory study to evaluate model components for prediction of human speech recognition in the presence of noise. Such a model has the potential to predict confusions between fine phonetic distinctions in different levels of background noise and at different speaking rates. The study takes advantage of modern physiological results that indicate that the primary auditory cortex performs spectro-temporal filtering; that is, that there are cells that are sensitive to particular spectro-temporal modulations at each auditory frequency. In this project, perceptual experiments in the presence of both stationary and non-stationary additive noise and at different signal-to-noise ratios for a database of CVC syllables recorded at 2 different speaking rates yield confusion statistics. These statistics are then compared to those resulting from an auditory model enhanced by elements incorporating these spectro-temporal filters. Successful results from this study will suggest enhancements to current hearing models and ultimately, after a broader study for which this EAGER is a pilot, advance the understanding of human speech perception. Background noise presents a challenging problem for a variety of speech and hearing devices including hearing aids and automatic speech recognition (ASR) systems. Since normal-hearing human listeners are extremely adept at perceiving speech in noise, this improved understanding of human models could lead to better artificial systems for speech processing. The databases and tools developed for this study will be disseminated to the research community.

这项探索性研究（急切）的早期概念赠款支持一项探索性研究，以评估模型组成部分，以预测在存在噪声下人类语音识别的。这样的模型有可能预测不同级别的背景噪声和不同语言速率的精细语音区分之间的混乱。该研究利用了现代的生理结果，表明主要的听觉皮层执行光谱式滤波。也就是说，在每个听觉频率下，有些细胞对特定的光谱传统调制敏感。在这个项目中，在存在固定和非平稳添加噪声的情况下，以及以不同的信噪比的比率进行的感知实验，对于以2种不同的口语速率记录的CVC音节数据库产生了混淆统计。然后，将这些统计数据与通过结合这些光谱频率过滤器的元素增强的听觉模型产生的统计数据进行比较。这项研究的成功结果将提出对当前听力模型的增强，并最终在一项更广泛的研究之后，这是一项渴望的飞行员，可以提高人们对人类语音感知的理解。背景噪声为各种语音和听力设备提供了一个具有挑战性的问题，包括助听器和自动语音识别（ASR）系统。由于正常听力的人听众非常擅长感知噪音中的语音，因此对人类模型的这种改善的理解可能会导致更好的人工系统进行语音处理。为这项研究开发的数据库和工具将被传播到研究界。