Query by example search with segmented dynamic time warping for non-exact spoken queries

This paper presents an approach to the Query-by-Example task of finding spoken queries on speech databases when the intended match may be non-exact or slightly complex. The built system is low-resource as it tries to solve the problem where the language of queries and searched audio is unspecified. Our method is based on a modified Dynamic Time Warping (DTW) algorithm using posterior-grams and extracting intricate paths to account for special cases of query match such as word re-ordering, lexical variations and filler content. This system was evaluated on the MediaEval 2014 task of Query by Example Search on Speech (QUESST) where the spoken data is from different languages, unknown to the participant. We combined the results of five DTW modifications computed on the output of three phoneme recognizers of different languages. The combination of all systems provided the best performance overall and improved detection of complex case queries.