Collaborative Research:EAGER:Deep Architectures for Speech and Audio Processing

最近的研究已经证明了深度架构在统计模式识别方面的强大能力。深度架构通过多层非线性处理来转换它们的输入。受生物神经网络连通性的启发，深层架构的隐藏层对复杂感官输入的分层、分布式表示进行编码。理论结果表明，这种表示需要解决人工智能中最困难的问题。深度架构以前的应用包括视觉对象识别、统计语言建模和非线性降维。在这些成功的基础上，该项目开发了用于语音和音频处理问题的深度架构的新应用。目前解决这些问题的前端主要采用传统的统计建模和信号处理方法。深度架构有潜力克服当前方法的许多限制。这个项目有两个相互关联和重叠的研究组成部分。该项目的第一个部分探索卷积神经网络中的无监督学习。在这些网络中学习的目标是发现音频事件检测和自动语音识别的新特征。该项目的第二个部分研究了在内核机器中进行深度学习的可能性。最近发现的一组核函数表明了这种可能性，这些核函数模拟了大型多层网络中的计算。该项目的研究部分与教育活动紧密结合。该项目资助了两名研究生，其中包括一名女学生。一个重要的目标是开发可供其他研究人员使用的公开软件。

Recent studies have demonstrated the powerful abilities of deep architectures for statistical pattern recognition. Deep architectures transform their inputs through multiple layers of nonlinear processing. Inspired by the connectivity of biological neural networks, the hidden layers of deep architectures encode hierarchical, distributed representations of complex sensory input. Theoretical results suggest that such representations are needed to solve the most difficult problems of artificial intelligence.Previous applications of deep architectures include visual object recognition, statistical language modeling, and nonlinear dimensionality reduction. Building on these successes, this project develops new applications of deep architectures for problems in speech and audio processing. Current front ends for these problems are dominated by traditional methods in statistical modeling and signal processing. Deep architectures have the potential to overcome many limitations of current approaches.This project has two research components with interrelated and overlapping goals. The project's first component explores unsupervised learning in convolutional neural networks. The goal of learning in these networks is to discover new features for audio event detection and automatic speech recognition. The project's second component investigates the possibility of deep learning in kernel machines. This possibility is suggested by a recently discovered family of kernel functions that mimic the computation in large, multilayer networks.The project's research components are tightly integrated with its educational activities. The project supports two graduate students, including one female student. An important goal is to develop publicly available software for use by other researchers.