Automatic error region detection and characterization in LVCSR transcriptions of TV news shows

This paper addresses the issue of error region detection and characterization in LVCSR transcriptions. It is a well-known phenomenon that errors are not independent and tend to co-occur in automatic transcriptions. We are interested in automatically detecting these so-called error regions. Additionally, in the context of information extraction in TVBN shows, being able to automatically characterize detected error regions is a crucial step towards the definition of suitable recovery strategies. In this paper we propose to classify error regions in four classes with a particular focus on errors on person names. We propose several sequential detection + classification approaches and an integrated sequence labeling approach. We show that our best classification system can reach 70% classification accuracy on automatically detected error regions. Additionally, the overall system is able to detect and correctly characterize 29.6% of error region corresponding to a person name with a precision of 61.9%.