A Multi-source Knowledge Fusion Strategy to Improve Confidence Measure in a Lattice-based Spoken Term Detection System

Abstract

This paper mainly concerns the problem of confldence measure estimation for Spoken Term Detection (STD). The detecting precision is always a main obstacle to make STD system to be applicable in realworld. In this context, a multi-source knowledge fusion strategy was proposed to improve the qualiflcation of confldence measure of detected spoken term which is mainly estimated by posterior probability before. For lattice based STD system, a collection of optimal predictive information of detected term is extracted, and the hidden-units Conditional Random Fields (hidden-units CRFs) is adopted to combine these information into a normalized conditional probability to stand for an alternative score of detected term. More precisely, the discriminative ability of multi-source knowledge fusion based confldence measure is proved to be superior to the posterior based confldence measure flrst. And then, the new proposed confldence measure was combined with the posterior to improve detecting precision and decrease False Alarm rate (FA) in a lattice-based STD system for Conversional Telephone Speech (CTS). Experimental results show that the new proposed confldence measure has strong complimentary efiect and improve the detecting precision about 17% over the baseline system in high precision area.