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

这个探索性研究的小赠款正在研究一种学习和遗忘算法的框架，该算法可以用于识别噪声稳健的听觉特征。尽管大多数基于语音的识别系统在受控的实验室条件下都能提供稳健的性能，但它们在存在噪声的情况下性能显著下降，这主要是由于训练和部署条件之间的不匹配。这项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，这些信息即使在环境噪声存在的情况下也可以保持完好。然而，在噪声存在的情况下估计这些非线性流形是一个巨大的挑战，也是本研究的重点。我们正在研究基于合作学习-解除学习(CLU)算法的概念验证特征，该算法估计由语音信号跨越的再生核Hilbert空间(RKHS)中的流形参数。我们正在评估这些特征在存在房间声学和背景噪声的情况下的稳健性。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，在该领域，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。这项研究的教育影响包括研究生培训和为CLU算法开发公共领域软件工具，这些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.