Speaker diarization of multi-party conversations using participants role information: political debates and professional meetings

Valente, F. and Vinciarelli, A. (2014) Speaker diarization of multi-party conversations using participants role information: political debates and professional meetings. In: Murray-Smith, R. (ed.) Mobile Social Signal Processing. Series: Lecture notes in computer science (8045). Springer: Heidelberg, pp. 22-33. ISBN 9783642543241 (doi:10.1007/978-3-642-54325-8_3)

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Publisher's URL: http://dx.doi.org/10.1007/978-3-642-54325-8_3

Abstract

Speaker Diarization aims at inferring who spoke when in an audio stream and involves two simultaneous unsupervised tasks: (1) the estimation of the number of speakers, and (2) the association of speech segments to each speaker. Most of the recent efforts in the domain have addressed the problem using machine learning techniques or statistical methods (for a review see [11]) ignoring the fact that the data consists of instances of human conversations.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Vinciarelli, Professor Alessandro
Authors: Valente, F., and Vinciarelli, A.
College/School:College of Science and Engineering > School of Computing Science
Publisher:Springer
ISBN:9783642543241
Copyright Holders:Copyright © 2014 The Authors

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