Recently, semi-supervised learning algorithms for phonetic classifiers have been proposed that have obtained promising results. Often, these algorithms attempt to satisfy learning criteria that are not inherent in the standard generative or discriminative training procedures for phonetic classifiers.
Graph-based learners in particular utilize an objective function that not only maximizes the classification accuracy on a labeled set but also the global smoothness of the predicted label assignment.
In this paper we investigate a novel graph-based semi-supervised learning framework that implements a controlled random walk where different possible moves in the random walk are controlled by probabilities that are dependent on the properties of the graph itself.
Experimental results on the TIMIT corpus are presented that demonstrate the effectiveness of this procedure.