Detecting and extracting named entities from spontaneous speech in a mixed-initiative spoken dialogue context: How May I Help You? sm, tm

Abstract

The understanding module of a spoken dialogue system must extract, from the speech recognizer output, the kind of request expressed by the caller (the call type) and its parameters (numerical expressions, time expressions or proper-names). Such expressions are called Named Entities and their definitions can be either generic or linked to the dialogue application domain. Detecting and extracting such Named Entities within a mixed-initiative dialogue context like How May I Help You? sm, tm (HMIHY) is the subject of this study. After reviewing standard methods based on hand-written grammars and statistical tagging, we propose a new approach, combining the advantages of both in a 2-step process. We also propose a novel architecture which exploits understanding to improve recognition accuracy: the output of the Automatic Speech Recognition module is now a word lattice and the understanding module is responsible for transcribing the word strings which are useful to the Dialogue Manager. All the methods proposed are trained and evaluated on a corpus comprising utterances from live customer traffic.