On-the-fly term spotting by phonetic filtering and request-driven decoding

This paper addresses the problem of on-the-fly term spotting in continuous speech streams. We propose a 2-level architecture in which recall and accuracy are sequentially optimized. The first level uses a cascade of phonetic filters to select the speech segments which probably contain the targeted terms. The second level performs a request-driven decoding of the selected speech segments. The results show good performance of the proposed system on broadcast news data : the best configuration reaches a F-measure of about 94% while respecting the on-the-fly processing constraint.