You are what you say: using meeting participants' speech to detect their roles and expertise

Authors:
Satanjeev Banerjee;Alexander I. Rudnicky
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
ACTS '09 Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
Year:
2006

Citing 5
Cited 5

Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Induction of Decision Trees

Machine Learning
A Comparative Study on Feature Selection in Text Categorization

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Discourse segmentation of multi-party conversation

ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The necessity of a meeting recording and playback system, and the benefit of topic–level annotations to meeting browsing

INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction

SmartNotes: implicit labeling of meeting data through user note-taking and browsing

NAACL-Demonstrations '06 Proceedings of the 2006 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume: demonstrations
Acquiring domain-specific dialog information from task-oriented human-human interaction through an unsupervised learning

EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Automatic decision detection in meeting speech

MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Participants' personal note-taking in meetings and its value for automatic meeting summarisation

Information Technology and Management
Detecting individual role using features extracted from speaker diarization results

Multimedia Tools and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Our goal is to automatically detect the functional roles that meeting participants play, as well as the expertise they bring to meetings. To perform this task, we build decision tree classifiers that use a combination of simple speech features (speech lengths and spoken keywords) extracted from the participants' speech in meetings. We show that this algorithm results in a role detection accuracy of 83% on unseen test data, where the random baseline is 33.3%. We also introduce a simple aggregation mechanism that combines evidence of the participants' expertise from multiple meetings. We show that this aggregation mechanism improves the role detection accuracy from 66.7% (when aggregating over a single meeting) to 83% (when aggregating over 5 meetings).