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

EARLY概念探索性研究资助（EAGER）支持一项探索性研究，以评估在噪声存在下预测人类语音识别的模型组件。这样的模型有可能预测在不同背景噪声水平和不同说话速率下的细微语音区别之间的混淆。这项研究利用了现代生理学的结果，这些结果表明初级听觉皮层执行频谱-时间滤波;也就是说，在每个听觉频率下，都有对特定频谱-时间调制敏感的细胞。在这个项目中，感知实验中存在的平稳和非平稳的加性噪声，并在不同的信号-噪声比的CVC音节的数据库中记录在2个不同的说话速率产生混淆统计。然后，这些统计数据进行比较，从一个听觉模型增强的元素，将这些频谱时间滤波器。这项研究的成功结果将建议增强当前的听力模型，并最终在EAGER作为试点的更广泛的研究之后，推进对人类语音感知的理解。背景噪声对于包括助听器和自动语音识别（ASR）系统在内的各种语音和听力设备来说是一个具有挑战性的问题。由于正常听力的人类听众非常擅长在噪音中感知语音，因此对人类模型的这种改进理解可能会导致更好的语音处理人工系统。为这项研究开发的数据库和工具将分发给研究界。

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.