Graph-Based Semi-Supervised Learning For Phone And Segment Classification

This paper presents several novel contributions to the emerging framework of graph-based semi-supervised learning for speech processing. First, we apply graph-based learning to variable-length segments rather than to the fixed-length vector representations that have been used previously. As part of this work we compare various graph-based learners, and we utilize an efficient feature selection technique for high-dimensional feature spaces that alleviates computational costs and improves the performance of graph-based learners. Finally, we present a method to improve regularization during the learning process. Experimental evaluation on the TIMIT frame and segment classification tasks demonstrates that the graph-based classifiers outperform standard baseline classifiers; furthermore, we find that the best learning algorithms are those that can incorporate prior knowledge.