Accurate keyword spotting using strictly lexical fillers
Rachida El Méliani, Douglas D. O’Shaughnessy · 2002
Our goal is to design an accurate keyword spotter that can deal with any size of keyword set, since the size actually required in a wide range of applications is large (number of airports, number of names in a directory, etc.). This justifies the choice of an architecture based on a large-vocabulary continuous-speech recognizer. In a previous paper we introduced the use of strictly-lexical subword fillers for keyword spotting based on the INRS large-vocabulary continuous-speech recognizer showing that they are, when compared to acoustic fillers, a good compromise between memory and time consumption, keyword choice freedom and task-independence training on one hand and accuracy on the other hand. We propose here two new high-performance designs of individual strictly-lexical subword fillers that perform, this time, better than their acoustic counterparts while still keeping the mentioned advantages.