Semantic entity detection in the spoken air traffic control data

Abstract

The paper deals with the semantic entity detection (SED) in the ASR lattices obtained by recognizing the air traffic control dialogs. The presented method is intended for the use in an automatic training tool for air traffic controllers. The semantic entities are modeled using the expert-defined context-free grammars. We use a novel approach which allows processing of uncertain input in the form of weighted finite state transducer. The method was experimentally evaluated on the real data. We also compare two methods for utilization of the knowledge about the dialog environment in the SED process. The results show that the SED with the knowledge about target semantic entities improves the equal error rate from 24.7% to 17.1% in comparison to generic SED.