Spoken Term Detection Using Distance-Vector based Dissimilarity Measures and Its Evaluation on the NTCIR-10 SpokenDoc-2 Task

In recent years, demands for distributing or searching multimedia contents are rapidly increasing and more effective method for multimedia information retrieval is desirable. In the studies on spoken document retrieval systems, much research has been presented focusing on the task of spoken term detection (STD), which locates a given search term in a large set of spoken documents. One of the most popular approaches performs indexing based on the sub-word sequence which is converted from the recognition hypotheses from LVCSR decoder for considering recognition errors and OOV problems. In this paper, we propose acoustic dissimilarity measures for improved STD performance. The proposed measures are based on a feature sequence of distance-vector representation, which consists of all the distances between two possible combinations of distributions in a set of subword unit HMMs and represents a structural feature. The experimental results showed that our two-pass STD system with new acoustic dissimilarity measure improve the performance compared to the STD system with a conventional acoustic measure.