Root cause analysis of miscommunication hotspots in spoken dialogue systems

Spiros Georgiladakis, Georgia Athanasopoulou, Raveesh Meena, José Lopes, Arodami Chorianopoulou, Elisavet Palogiannidi, Elias Iosif, Gabriel Skantze, Alexandros Potamianos

Research output: Contribution to journalConference articlepeer-review

Abstract

A major challenge in Spoken Dialogue Systems (SDS) is the detection of problematic communication (hotspots), as well as the classification of these hotspots into different types (root cause analysis). In this work, we focus on two classes of root cause, namely, erroneous speech recognition vs. other (e.g., dialogue strategy). Specifically, we propose an automatic algorithm for detecting hotspots and classifying root causes in two subsequent steps. Regarding hotspot detection, various lexico-semantic features are used for capturing repetition patterns along with affective features. Lexico-semantic and repetition features are also employed for root cause analysis. Both algorithms are evaluated with respect to the Let's Go dataset (bus information system). In terms of classification unweighted average recall, performance of 80% and 70% is achieved for hotspot detection and root cause analysis, respectively.

Original languageEnglish
Pages (from-to)1156-1160
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume08-12-September-2016
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States
Duration: 8 Sept 201616 Sept 2016

Keywords

  • Miscommunication detection
  • Miscommunication root causes
  • Spoken dialogue systems

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