Large Margin Algorithms and Kernel Methods for Speech Recognition

Research on large margin algorithms in conjunctions with kernel methods has been both exciting and successful. While there have been quite a few preliminary successes in applying kernel methods for speech applications, most research efforts have focused on non-temporal problems such as text classification and optical character recognition (OCR). We propose to design, analyze, and implement learning algorithms and kernels for hierarchical-temporal speech utterances. Our first and primary end-goal is to build and test thoroughly a full-blown speech phoneme classifier that will be trained on millions of examples and will achieve the best results in this domain. This project is a joint reseach effort between The Hebrew University and IDIAP.