SGER: Cooperative Learning-unlearning Algorithms for Identification of Robust Auditory Manifolds

这项探索性研究的小额资助正在研究一个学习和非学习算法的框架，该框架可用于识别噪声鲁棒的听觉特征。尽管大多数基于语音的识别系统在受控的实验室条件下提供鲁棒的性能，但它们的性能在噪声存在下会显着下降，主要是由于训练和部署条件之间的不匹配。拟议的探索性研究调查了使用嵌入在高阶谱和时间流形中的信息的可能性，即使在存在环境噪声的情况下，这些信息也可以保持完整。这些非线性流形的存在噪声的估计，但是，构成了一个重大的挑战，是本研究的重点。我们正在研究概念验证功能的基础上合作学习-非学习（CLU）算法，估计流形参数的再生核希尔伯特空间（RKHS）跨越语音信号。我们正在评估这些功能在室内声学和背景噪声存在下的鲁棒性。这项探索性研究的更广泛影响将是开发可用于基于语音的生物识别领域的使能技术，其中可以通过互联网，手机或其他基于语音的媒体进行无缝认证。该研究的教育影响包括研究生培训和CLU算法的公共领域软件工具的开发，这些软件工具将提供给科学界。

This Small Grant for Exploratory Research is investigating a framework of learning and unlearning algorithms that can be used for identifying noise robust auditory features.Even though most speech based recognition systems deliver robust performance under controlled laboratory conditions, their performance degrades significantly in presence of noise primarily due to mismatch between training and deployment conditions. The proposed exploratory study investigates the possibility of using information embedded in higher-order spectral and temporal manifolds which could remain intact even in the presence of ambient noise. Estimation of these non-linear manifolds in presence of noise, however, poses a significant challenge and is the focus of this study. We are investigating proof-of-concept features based on cooperative learning-unlearning (CLU) algorithms that estimates manifold parameters in a reproducing kernel Hilbert space (RKHS) spanned by speech signals. We are evaluating the robustness of these features in presence of room acoustics and background noise. The broader impact of this exploratory study will be development of enabling technology that can be used in the area of voice based biometrics, where seamless authentication can be performed over the internet, cell phones or other voice based media. The educational impact of the study includes graduate student training and development of public domain software tools for CLU algorithms which will be available to the scientific community.