Date & Time:
Tuesday, May 7, 2013; 2:30 PM
Kernel methods are important to realize both convexity in estimation and ability to represent nonlinear classification. However, in automatic speech recognition fields, kernel methods are not widely used conventionally. In this presentation, I will introduce several attempts to practically incorporate kernel methods into acoustic models for automatic speech recognition. The presentation will consist of two parts. The first part will describes maximum entropy discrimination and its application to a kernel machine training. The second part will describes dimensionality reduction of kernel-based features.
Dr. Yotaro Kubo
NTT Communication Science Laboratories, Kyoto, Japan
Yotaro Kubo is a research staff at NTT Communication Science Laboratories, Kyoto, Japan. He received the B.E., M.E., and Dr. Eng. degrees from Waseda University, Tokyo, Japan, in 2007, 2008, and 2010, respectively. He was a visiting scientist at RWTH Aachen University from April to October 2010. In 2010, he joined NTT and has been with NTT Communication Science Laboratories. His research interest includes machine learning for signal processing.