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Pattern Analysis and Applications

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23.03.2017 | Short Paper

A feature selection-based speaker clustering method for paralinguistic tasks

verfasst von: Gábor Gosztolya, László Tóth

Erschienen in: Pattern Analysis and Applications | Ausgabe 1/2018

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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.

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Titel: A feature selection-based speaker clustering method for paralinguistic tasks
verfasst von: Gábor Gosztolya
László Tóth
Publikationsdatum: 23.03.2017
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 1/2018
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-017-0612-0

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