Impact of spontaneous speech features on business concept detection: a study of call-centre data.

Abstract

This paper focuses on the detection of business concepts in call-centre conversation transcriptions. In the literature, information extraction behavior has been rarely deeply analyzed on such spontaneous speech data. We highlight here the various problems that are encountered when we attempt to extract information from such data. The recall and precision, which are obtained by comparing the concept detection method on automatic vs. manual transcription, are respectively at 74.8% and 77.7%. We find that, even though the concept detection is similar on the whole between manual and automatic transcriptions, spontaneous speech features tend to cause different behaviors of opinion-related concept detection on both transcriptions. On the one hand, spontaneous speech features, which frequently occur in these data, provokes silence (lack of detection) when detecting concepts on both transcriptions. On the other hand, ASR errors (e.g. due to homophony or disfluencies) tend to provoke noise (excessive detection) when detecting concept on automatic transcription.