Domain-independent topic segmentation using a string kernel on recognized sub-word sequences

The goal of the present paper is to explore the feasibility of a topic segmentation method without using large vocabulary continuous speech recognition (LVCSR). The proposed method is domain-independent in the sense that it is not constrained by vocabulary and does not require training data. For a sequence of sub-word units obtained using a continuous sub-word recognizer, the proposed method merges similar adjacent parts of the sequence in an agglomerative manner to produce a hierarchical cluster tree. The proposed method uses a string kernel to efficiently compute the similarity between two strings of sub-word units based on the frequencies of any sub-strings appearing in the strings. By carefully excluding the influence of the sub-strings that are irrelevant to the topic of interest, topically coherent clusters are formed without linguistic knowledge. An empirical study on a Japanese news speech corpus shows that the method performs better than a topic segmenter using LVCSR.