Contextual verification for open vocabulary spoken term detection

Abstract

In spoken term detection, subword speech recognition is a viable means for addressing the out-of-vocabulary (OOV) problem at query time. Applying fuzzy error compensation techniques is needed for coping with inevitable recognition errors, but can lead to high false alarm rates especially for short queries. We propose two novel methods which reject false alarms based on the context of the hypothesized result and the distance to phonetically similar queries. Using the proposed methods, we obtain an increase in precision of 11% absolute at equal recall.