AUDIAS-CEU: A Language-independent approach for the Query-by-Example Spoken Term Detection task of the Search on Speech ALBAYZIN 2018 evaluation

Abstract

Query-by-Example Spoken Term Detection is the task of detecting query occurrences within speech data (henceforth utterances). Our submission is based on a language-independent template matching approach. First, queries and utterances are represented as phonetic posteriorgrams computed for English language with the phoneme decoder developed by the Brno Uni-versity of Technology. Next, the Subsequence Dynamic Time Warping algorithm with a modified Pearson correlation coefficient as cost measure is employed to hipothesize detections. Results on development data showed an ATWV=0.1774 with MAVIR data and an ATWV=0.0365 with RTVE data.