Spotting using Durational Entropy

This paper deals with the task of detection of a given keyword in continuous speech. We build upon a previously proposed algorithm where a modified Viterbi search algorithm is used to detect keywords, without requiring any explicit garbage or filler models. In this work, the concept of durational entropy is used to further discard a large fraction of false alarm errors. Durational entropy is defined as the entropy of the distribution of state occupancies. A method to recursively compute it for all Viterbi paths is also presented in this paper. Experimental results on one hour of broadcast news data suggest that durational entropy constraints can indeed be used to avoid a large number of false alarms errors at a minimal cost of degradation in keyword detection accuracy.