A novel keyword rescoring method for improved spoken keyword spotting

Abstract

In this paper, we present a spoken KeyWord Spotting (KWS) system which creates a search index from word lattices generated by a deep speech recognizer. Basic KWS systems estimate word posteriors from the lattices and use them to make “correct/false alarm” decisions. The main issue of lattice-based posterior probability is that a putative detection can have very low posterior probability so that the decider fails to detect it and considers it as a false alarm. Therefore, our goal is to enhance the keyword decision by detecting and boosting the score of missed detections. Accordingly, inspired by template matching approach, we propose a new keyword rescoring method. More precisely, detected hits are rescored based on the acoustic similarity and the new score are used then by the decider to make the final decision. Experiments demonstrate that the proposed method potentially leads to more accurate keyword results than the conventional KWS system.