Spoken Term Detection Based on Improved Index Structure

Abstract

The performance of keyword spotting system suffers severe degradation when the index stage is so fast that the lattice may lose lots of information to retrieve the spoken terms. In this paper, we focus on this problem and present two algorithm: the first one called unconstraint word graph expansion (UWGE) and the other called dynamic position specific posterior lattice(D-PSPL). The motivation of these methods is to keep the pruned hypotheses which are discarded in the decoding procedure but may contain correct hypotheses. The proposed approaches is to eliminate the N-gram language model state limitation of lattice and reconstruct lattice to unconstrained word graph. On two Mandarin conversation telephone speech sets, we compare performance using the two methods with that on traditional trigram lattice, and our approaches give satisfying performance gains over trigram lattice. The experiment results also show that the D-PSPL algorithm is better than the UWGE algorithm in high score area.