First Experiments to Detect Anomaly Using Personality Traits vs. Prosodic Features

  • Cedric Fayet
  • Arnaud Delhay
  • Damien Lolive
  • Pierre-François Marteau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10458)


This paper presents the design of an anomaly detector based on three different sets of features, one corresponding to some prosodic descriptors and two extracted from Big Five traits. Big Five traits correspond to a simple but efficient representation of a human personality. They are extracted from a manual annotation while prosodic features are extracted directly from the speech signal. We evaluate two different anomaly detection methods: One-Class SVM (OC-SVM) and iForest, 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 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. In our case, OC-SVM seems to get better results than iForest.


Anomaly detection Isolation Forest Isolation Tree One Class – Support Vector Machine Threshold classification Social signal Big Five Prosody SSPNET-Personality 



This research has been financially supported by the French Ministry of Defense - Direction Générale pour l’Armement and the région Bretagne (ARED) under the MAVOFA project.


  1. 1.
    Abdallah, A., Maarof, M.A., Zainal, A.: Fraud detection system: a survey. J. Netw. Comput. Appl. 68, 90–113 (2016)CrossRefGoogle Scholar
  2. 2.
    Alonso, J.B., Díaz-de María, F., Travieso, C.M., Ferrer, M.A.: Using nonlinear features for voice disorder detection. In: ISCA Tutorial and Research Workshop (ITRW) on Non-linear Speech Processing (2005)Google Scholar
  3. 3.
    Axelsson, S.: Intrusion detection systems: A survey and taxonomy, Technical report. Chalmers University of Technology, Göteborg, Sweden (2000)Google Scholar
  4. 4.
    Boersma, P., Weenink, D.: Praat: doing phonetics by computer,
  5. 5.
    Caliski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat.-Theory Methods 3(1), 1–27 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  7. 7.
    Clapham, R.P., van der Molen, L., van Son, R.J.J.H., van den Brekel, M.W.M., Hilgers, F.J.M.: NKI-CCRT corpus - speech intelligibility before and after advanced head and neck cancer treated with concomitant chemoradiotherapy. In: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC) (2012)Google Scholar
  8. 8.
    Goldberg, L.R.: Language and individual differences: the search for universals in personality lexicons. Rev. Pers. Soc. Psychol. 2(1), 141–165 (1981)Google Scholar
  9. 9.
    Gurven, M., von Rueden, C., Massenkoff, M., Kaplan, H., Lero Vie, M.: How universal is the big five? testing the five-factor model of personality variation among forager farmers in the bolivian amazon. J. Pers. Soc. Psychol. 104(2), 354–370 (2013)CrossRefGoogle Scholar
  10. 10.
    He, L., Lech, M., Maddage, N.C., Allen, N.: Stress detection using speech spectrograms and sigma-pi neuron units. In: Fifth International Conference on Natural Computation, ICNC 2009. vol. 2, pp. 260–264. IEEE (2009)Google Scholar
  11. 11.
    John, O.P., Srivastava, S.: The big five trait taxonomy: history, measurement, and theoretical perspectives, vol. 2, pp. 102–138. Guilford (1999)Google Scholar
  12. 12.
    Li, L., Gariel, M., Hansman, R.J., Palacios, R.: Anomaly detection in onboard-recorded flight data using cluster analysis. In: IEEE/AIAA 30th Digital Avionics Systems Conference, p. 4A4-1–4A4-11. IEEE (2011)Google Scholar
  13. 13.
    Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: Eighth IEEE International Conference on Data Mining, pp. 413–422 (2008)Google Scholar
  14. 14.
    Mohammadi, G., Vinciarelli, A.: Automatic personality perception: prediction of trait attribution based on prosodic features. IEEE Trans. Affect. Comput. 3(3), 273–284 (2012)CrossRefGoogle Scholar
  15. 15.
    Park, K., Lin, Y., Metsis, V., Le, Z., Makedon, F.: Abnormal human behavioral pattern detection in assisted living environments. In: Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments, p. 9 (2010)Google Scholar
  16. 16.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Rammstedt, B., John, O.P.: Measuring personality in one minute or less: a 10-item short version of the big five inventory in english and german. J. Res. Pers. 41(1), 203–212 (2007)CrossRefGoogle Scholar
  18. 18.
    Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)CrossRefzbMATHGoogle Scholar
  19. 19.
    Schuller, B., Steidl, S., Batliner, A., Nth, E., Vinciarelli, A., Burkhardt, F., van Son, R., Weninger, F., Eyben, F., Bocklet, T., Mohammadi, G., Weiss, B.: A survey on perceived speaker traits: Personality, likability, pathology, and the first challenge. Comput. Speech Lang. 29(1), 100–131 (2015)CrossRefGoogle Scholar
  20. 20.
    Valstar, M., Gratch, J., Schuller, B., Ringeval, F., Lalanne, D., Torres Torres, M., Scherer, S., Stratou, G., Cowie, R., Pantic, M.: AVEC 2016: Depression, mood, and emotion recognition workshop and challenge. In: Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge, pp. 3–10. ACM (2016)Google Scholar
  21. 21.
    Vinciarelli, A., Mohammadi, G.: A survey of personality computing. IEEE Trans. Affect. Comput. 5(3), 273–291 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Cedric Fayet
    • 1
    • 2
  • Arnaud Delhay
    • 1
    • 2
  • Damien Lolive
    • 1
    • 2
  • Pierre-François Marteau
    • 1
    • 3
  1. 1.IRISA - EXPRESSION TeamLannion and VannesFrance
  2. 2.Université de Rennes 1LannionFrance
  3. 3.Université de Bretagne SudVannesFrance

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