Improved spoken term detection with graph-based re-ranking in feature space

This paper presents a graph-based approach for spoken term detection. Each first-pass retrieved utterance is a node on a graph and the edge between two nodes is weighted by the similarity between the two utterances evaluated in feature space. The score of each node is then modified by the contributions from its neighbors by random walk or its modified version, because utterances similar to more utterances with higher scores should be given higher relevance scores. In this way the global similarity structure of all first-pass retrieved utterances can be jointly considered. Experimental results show that this new approach offers significantly better performance than the previously proposed pseudo-relevance feedback approach, which considers primarily the local similarity relationship between first-pass retrieved utterances, and these two different approaches can be cascaded to provide even better results.