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Pattern Analysis and Applications

, Volume 21, Issue 1, pp 193–204 | Cite as

A feature selection-based speaker clustering method for paralinguistic tasks

  • Gábor Gosztolya
  • László Tóth
Short Paper

Abstract

In recent years, computational paralinguistics has emerged as a new topic within speech technology. It concerns extracting non-linguistic information from speech (such as emotions, the level of conflict, whether the speaker is drunk). It was shown recently that many methods applied here can be assisted by speaker clustering; for example, the features extracted from the utterances could be normalized speaker-wise instead of using a global method. In this paper, we propose a speaker clustering algorithm based on standard clustering approaches like K-means and feature selection. By applying this speaker clustering technique in two paralinguistic tasks, we were able to significantly improve the accuracy scores of several machine learning methods, and we also obtained an insight into what features could be efficiently used to separate the different speakers.

Keywords

Computational paralinguistics Clustering Speaker clustering Feature selection Classifier combination Support-vector machines Deep neural networks AdaBoost.MH 

Notes

Acknowledgements

This publication is supported by the European Union and co-funded by the European Social Fund. Project title: Telemedicine-oriented research activities in the fields of mathematics, informatics and medical sciences. Project number: TÁMOP-4.2.2.A-11/1/KONV-2012-0073.

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Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.MTA-SZTE Research Group on Artificial Intelligence of the Hungarian Academy of SciencesUniversity of SzegedSzegedHungary
  2. 2.Institute of Informatics, University of SzegedSzegedHungary

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