Divergence-Based Similarity Measure for Spoken Document Retrieval

We propose a novel, divergence-based similarity measure for spoken document retrieval (SDR). We derive a dynamic programming algorithm that measures Kullback-Leibler divergence between two HMMs first. The measure is further generalized to a graph matching algorithm, which is efficient for SDR application. The proposed approach compares the underlying acoustic models of keywords and a target database to alleviate the impact of mismatched vocabulary and language model, e.g. different domains. Experimental results on the Wall Street Journal (WSJ) database show that the proposed approach achieves a comparable performance, compared with the word posterior based approach. It outperforms the latter when there is a mismatch in language model. The approach is promising for building an open-vocabulary, domain independent SDR application.