Big Five vs. Prosodic Features as Cues to Detect Abnormality in SSPNET-Personality Corpus

Cédric Fayet 1 Arnaud Delhay 1 Damien Lolive 1 Pierre-François Marteau 1
1 EXPRESSION - Expressiveness in Human Centered Data/Media
UBS - Université de Bretagne Sud, IRISA-D6 - MEDIA ET INTERACTIONS
Abstract : This paper presents an attempt to evaluate three different sets of features extracted from prosodic descriptors and Big Five traits for building an anomaly detector. The Big Five model enables to capture personality information. Big Five traits are extracted from a manual annotation while Prosodic features are extracted directly from the speech signal. Two different anomaly detection methods are evaluated: Gaussian Mixture Model (GMM) and One-Class SVM (OC-SVM), each one combined with a threshold classification to decide the ”normality” of a sample. The different combinations of models and feature sets are evaluated on the SSPNET-Personality corpus which has already been used in several experiments, including a previous work on separating two types of personality profiles in a supervised way. In this work, we propose the above mentioned unsupervised or semi-supervised methods, and discuss their performance, to detect particular audio-clips produced by a speaker with an abnormal personality. Results show that using automatically extracted prosodic features competes with the Big Five traits. The overall detection performance achieved by the best model is around 0.8 (F1-measure)
Type de document :
Communication dans un congrès
Interspeech, Aug 2017, Stockholm, Sweden. 2017, 〈http://www.interspeech2017.org/〉
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https://hal.inria.fr/hal-01583510
Contributeur : Expression Irisa <>
Soumis le : jeudi 7 septembre 2017 - 14:17:54
Dernière modification le : vendredi 16 novembre 2018 - 01:40:31

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  • HAL Id : hal-01583510, version 1

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Cédric Fayet, Arnaud Delhay, Damien Lolive, Pierre-François Marteau. Big Five vs. Prosodic Features as Cues to Detect Abnormality in SSPNET-Personality Corpus. Interspeech, Aug 2017, Stockholm, Sweden. 2017, 〈http://www.interspeech2017.org/〉. 〈hal-01583510〉

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