FDVQ based keyword spotter which incorporates a semi-supervised learning for primary processing
Chakib Tadj, Pierre Dumouchel, Franck Poirier · 1997
In this paper, we present a novel hybrid keyword spotting system that combines supervised and semi-supervised competitive learning algorithms. The rst stage is a S-SOM (Semi-supervised SelfOrganizing Map) module which is speci cally designed for discrimination between keywords (KWs) and non-keywords (NKWs). The second stage is an FDVQ (Fuzzy Dynamic Vector Quantization) module which consists of discriminating between KWs detected by the rst stage processing. The experiment on Switchboard database has show an improvement of about 6% on the accuracy of the system comparing to our best keyword-spotter one.