A two-step keyword spotting method based on context-dependent a posteriori probability

Abstract

Keyword weighting plays an important role in traditional keyword spotting (KWS) systems: it helps detect keyword candidates in an utterance so that they will not be missed. However, if the keywords are over-weighted, there will be a high number of false alarms, which will slow down the system and might introduce rejection errors; on the other hand, if the keywords are insufficiently weighted, the detection rate is not guaranteed. It is difficult to make a compromise with regard to keyword weighting. A two-step KWS method based on context-dependent a posteriori probability (CDAPP) is proposed in this paper as a way to solve this problem. The first step adopts a continuous speech recognition method, to generate a sequence of acoustic symbols for the second step, which performs a fuzzy keyword search. Preliminary experiments show that the proposed strategy is a promising one that needs additional investigation.