Spoken Term Detection Based on Unconstrained Word Graph Extension

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 an algorithm called Unconstraint Word Graph Expansion (UWGE) to keep the pruned hypotheses which are discarded in the decoding procedure but may contain correct hypotheses. The proposed approach 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 UWGE with that on traditional trigram lattice, and our approach gives satisfying performance gains over trigram lattice. We also show the relationship between the performance and the system speed based on this approach.