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

这项探索性研究的早期概念资助（EAGER）支持一项探索性研究，以评估在存在噪声的情况下预测人类语音识别的模型成分。这样的模型有可能预测在不同程度的背景噪音和不同的说话速度下细微语音差别之间的混淆。该研究利用了现代生理学的结果，表明初级听觉皮层执行光谱-时间过滤；也就是说，有些细胞对每个听觉频率的特定光谱-时间调制非常敏感。在这个项目中，在平稳和非平稳加性噪声的存在下，以及在不同的信噪比下，对以两种不同语速记录的CVC音节数据库进行感知实验，产生混淆统计。然后将这些统计数据与由包含这些光谱-时间滤波器的元素增强的听觉模型的结果进行比较。这项研究的成功结果将对当前的听力模型提出改进建议，并最终在更广泛的研究之后，推进对人类语言感知的理解。背景噪声对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。由于听力正常的人类听众非常擅长在噪音中感知语音，因此对人类模型的理解的提高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.