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 University 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.