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

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).

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Cite this paper

@inproceedings{Fayet2017BigFV, title={Big Five vs. Prosodic Features as Cues to Detect Abnormality in SSPNET-Personality Corpus}, author={Cedric Fayet and Arnaud Delhay and Damien Lolive and Pierre-François Marteau}, year={2017} }