Automatic Detection of Semantic Boundaries

In spoken language systems, the segmentation of utterances into coherent linguistic/semantic units is very useful, as it makes easier processing after the speech recognition phase. In this paper, a methodology for semantic boundary prediction is presented and tested on a corpus of person-to-person dialogues. The approach is based on binary decision trees and uses text context, including broad classes of silent pauses, filled pauses and human noises. Best results give more than 90% precision, almost 80% recall and about 3% false alarm