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

这项早期概念探索性研究补助金(AGER)支持一项探索性研究，以评估在噪声存在的情况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水平的背景噪音和不同的语速下细微的语音区别之间的混淆。这项研究利用了现代生理学结果，这些结果表明，初级听觉皮质执行谱-时间滤波；也就是说，在每个听觉频率上，有对特定谱-时间调制敏感的细胞。在这个项目中，对于一个以两种不同语速记录的CVC音节数据库，在存在平稳和非平稳加性噪声以及不同信噪比的情况下进行的知觉实验产生了混淆统计数据。然后，将这些统计数据与由结合了这些谱-时间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建议对目前的听力模型进行增强，并最终在本研究机构作为试点的更广泛的研究之后，促进对人类言语感知的理解。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者非常擅长在噪音中感知语音，这种对人类模型的理解的改进可能会导致更好的人工语音处理系统。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.