Abstract
Decision making is an important aspect of meetings in organisational settings, and archives of meeting recordings constitute a valuable source of information about the decisions made. However, standard utilities such as playback and keyword search are not sufficient for locating decision points from meeting archives. In this paper, we present the AMI DecisionDetector, a system that automatically detects and highlights where the decision-related conversations are. In this paper, we apply the models developed in our previous work [1], which detects decision-related dialogue acts (DAs) from parts of the transcripts that have been manually annotated as extract-worthy, to the task of detecting decision-related DAs and topic segments directly from complete transcripts. Results show that we need to combine features extracted from multiple knowledge sources (e.g., lexical, prosodic, DA-related, and topical class) in order to yield the model with the highest precision. We have provided a quantitative account of the feature class effects. As our ultimate goal is to operate AMI DecisionDetector in a fully automatic fashion, we also investigate the impacts of using automatically generated features, for example, the 5-class DA features obtained in [2].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hsueh, P., Moore, J.: What decisions have you made: Automatic decision detection in conversational speech. In: Proceedings of NACCL/HLT 2007 (2007)
Dielmann, A., Renals, S.: DBN based joint dialogue act recognition of multiparty meetings. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing (2007)
Pallotta, V., Niekrasz, J., Purver, M.: Collaborative and argumentative models of meeting discussions. In: IJCAI 2005. Proceeding of CMNA-05 workshop on Computational Models of Natural Arguments (2005)
Rienks, R., Heylen, D., van der Weijden, E.: Argument diagramming of meeting conversations. In: Multimodal Multiparty Meeting Processing Workshop at the ICMI (2005)
Pallotta, V., Seretan, V., Ailomaa, M.: User requirements analysis for meeting information retrieval based on query elicitation. In: Proceedings of ACL 2007 (2007)
Banerjee, S., Rose, C., Rudnicky, A.I.: The necessity of a meeting recording and playback system, and the benefit of topic-level annotations to meeting browsing. In: Proc. of the International Conference on Human-Computer Interaction (2005)
Whittaker, S., Laban, R., Tucker, S.: Analysing meeting records: An ethnographic study and technological implications. In: Renals, S., Bengio, S. (eds.) MLMI 2005. LNCS, vol. 3869, Springer, Heidelberg (2006)
Post, W.M., Cremers, A.H., Henkemans, O.B.: A research environment for meeting behavior. In: Proceedings of the 3rd Workshop on Social Intelligence Design (2004)
Romano, N.C., Nunamaker, J.F.: Meeting analysis: Findings from research and practice. In: Proceedings of HICSS-34, IEEE Computer Society Press, Los Alamitos (2001)
Niekrasz, J., Purver, M., Dowding, J., Peters, S.: Ontology-based discourse understanding for a persistent meeting assistant. In: Proc. of the AAAI Spring Symposium (2005)
Marchand-Mailet, S.: Meeting record modeling for enhanced browsing. Technical report, Computer Vision and Multimedia Lab, Computer Centre, University of Geneva, Switzerland (2003)
Galley, M., McKeown, J., Hirschberg, J., Shriberg, E.: Identifying agreement and disagreement in conversational speech: Use of bayesian networks to model pragmatic dependencies. In: Proc. of the 42nd Annual Meeting of the ACL (2004)
Gatica-Perez, D., McCowan, I., Zhang, D., Bengio, S.: Detecting group interest level in meetings. In: ICASSP 2005. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, IEEE Computer Society Press, Los Alamitos (2005)
Hillard, D., Ostendorf, M., Shriberg, E.: Detection of agreement vs. disagreement in meetings: Training with unlabeled data. In: Proc. HLT-NAACL 2003 (2003)
Purver, M., Ehlen, P., Niekrasz, J.: Shallow discourse structure for action item detection. In: The Workshop of HLT-NAACL: Analyzing Conversations in Text and Speech, ACM Press, New York (2006)
Wrede, B., Shriberg, E.: Spotting hot spots in meetings: Human judgements and prosodic cues. In: Proceedings of EUROSPEECH 2003 (2003)
Carletta, J., et al.: The AMI meeting corpus: A pre-announcement. In: Proceedings of 2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms (2005)
Hsueh, P., Moore, J.D.: Combining multiple knowledge sources for dialogue segmentation in multimedia archives. In: Proceedings of the 45th Annual Meeting of the ACL (2007)
McCowan, I., Gatica-Perez, D., Bengio, S., Lathoud, G., Barnard, M., Zhang, D.: Automatic analysis of multimodal group actions in meetings. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 27(3), 305–317 (2005)
Zhang, D., Gatica-Perez, D., Bengio, S., McCowan., I.: Semi-supervised adapted hmms for unusual event detection. In: CVPR 2005. Proc. IEEE International Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2005)
Banerjee, S., Rudnicky, A.I.: You are what you say: Using meeting participants’ speech to detect their roles and expertise. In: Proceedings of the Workshop of HLT-NAACL: Analyzing Conversations in Text and Speech, ACM Press, New York (2006)
Jovanovic, N., op den Akker, R., Nijholt, A.: Addressee identification in face-to-face meetings. In: The Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (2006)
Graciarena, M., Shriberg, E., Stolcke, A., Enos, F., Hirschberg, J., Kajarekar, S.: Combining prosodic, lexical and cepstral systems for deceptive speech detection. In: Proc. IEEE ICASSP, IEEE Computer Society Press, Los Alamitos (2006)
Wrede, B., Shriberg, E.: The relationship between dialogue acts and hot spots in meetings. In: Proceedings of IEEE ASRU Workshop, IEEE Computer Society Press, Los Alamitos (2003)
Litman, D.J., Forbes-Riley, K.: Recognizing student emotions and attitudes on the basis of utterances in spoken tutoring dialogues with both human and computer tutors. Speech Communication (2006)
Shriberg, E., Stolcke, A.: Direct modeling of prosody: An overview of applications in automatic speech processing. In: International Conference on Speech Prosody 2004 (2001)
Murray, G., Renals, S., Taboada, M.: Prosodic correlates of rhetorical relations. In: Proceedings of HLT/NAACL ACTS Workshop (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hsueh, PY., Moore, J.D. (2008). Automatic Decision Detection in Meeting Speech. In: Popescu-Belis, A., Renals, S., Bourlard, H. (eds) Machine Learning for Multimodal Interaction. MLMI 2007. Lecture Notes in Computer Science, vol 4892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78155-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-78155-4_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78154-7
Online ISBN: 978-3-540-78155-4
eBook Packages: Computer ScienceComputer Science (R0)