Using Speech Data to Recognize Emotion in Human Gait

  • Angelica Lim
  • Hiroshi G. Okuno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7559)

Abstract

Robots that can recognize emotions can improve humans’ mental health by providing empathy and social communication. Emotion recognition by robots is challenging because unlike in human-computer environments, facial information is not always available. Instead, our method proposes using speech and gait analysis to recognize human emotion. Previous research suggests that the dynamics of emotional human speech also underlie emotional gait (walking). We investigate the possibility of combining these two modalities via perceptually common parameters: Speed, Intensity, irRegularity, and Extent (SIRE). We map low-level features to this 4D cross-modal emotion space and train a Gaussian Mixture Model using independent samples from both voice and gait. Our results show that a single, modality-mixed trained model can perform emotion recognition for both modalities. Most interestingly, recognition of emotion in gait using a model trained uniquely on speech data gives comparable results to a model trained on gait data alone, providing evidence for a common underlying model for emotion across modalities.

Keywords

robot emotions emotional gait emotional voice affect recognition 

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References

  1. 1.
    Clynes, M.: Sentics: The Touch of the Emotions. Prism Press, UK (1989)Google Scholar
  2. 2.
    Pollick, F.E., Paterson, H.M., Bruderlin, A., Sanford, A.J.: Perceiving affect from arm movement. J. Personal. 82, 51–61 (2001)Google Scholar
  3. 3.
    Van Bezooijen, R., Van Otto, S.A., Heenan, T.A.: Recognition of vocal dimensions of emotion: A three-nation study to identify universal characteristics. J. Cross-Cultural Psych. 14, 387–406 (1983)CrossRefGoogle Scholar
  4. 4.
    Juslin, P.N., Laukka, P.: Communication of emotions in vocal expression and music performance: different channels, same code? Psychol. Bull. 129(5), 770–814 (2003)CrossRefGoogle Scholar
  5. 5.
    Spencer, H.: The origin and function of music. Fraser’s Magazine 56, 396–408 (1857)Google Scholar
  6. 6.
    Scherer, K.H.: Vocal affect expression: A review and a model for future research. Psychol. Bull. 99, 143–165 (1986)CrossRefGoogle Scholar
  7. 7.
    Snowdon, C.T.: Expression of emotion in non-human animals. In: Davidson, R.J., Sherer, K.H., Goldsmith, H.H. (eds.) Handbook of affective sciences, pp. 457–480. Oxford University Press, London (2003)Google Scholar
  8. 8.
    Breazeal, C.: Designing sociable robots, 1st edn. The MIT Press, Cambridge (2004)Google Scholar
  9. 9.
    Lim, A., Ogata, T., Okuno, H.G.: Towards expressive musical robots: a cross-modal framework for emotional gesture, voice and music. EURASIP J. Audio, Speech, and Music Proc. 2012(3) (2012)Google Scholar
  10. 10.
    Lim, A., Ogata, T., Okuno, H.G.: Converting emotional voice to motion for robot telepresence. In: Humanoids, Bled, pp. 472–479 (2011)Google Scholar
  11. 11.
    Cowie, R., et al.: Emotion recognition in human-computer interaction. IEEE Signal Proc. Magazine 18(1), 32–80 (2001)CrossRefGoogle Scholar
  12. 12.
    Fernandez, R., Picard, R.W.: Classical and Novel Discriminant Features for Affect Recognition from Speech. In: INTERSPEECH, pp. 4–8 (2005)Google Scholar
  13. 13.
    Mion, L., De Poli, G.: Score-independent audio features for description of music expression. IEEE Trans. Audio Speech Lang. Process. 16(2), 458–466 (2008)CrossRefGoogle Scholar
  14. 14.
    Livingstone, S.R., Brown, A.R., Muhlberger, R., Thompson, W.F.: Modifying score and performance changing musical emotion: a computational rule system for modifying score and performance. Comput. Music J. 34(1), 41–65 (2010)CrossRefGoogle Scholar
  15. 15.
    Amaya, K., Bruderlin, A., Calvert, T.: Emotion from motion. Graph. In: Interface, pp. 222–229 (1996)Google Scholar
  16. 16.
    Pelachaud, C.: Studies on gesture expressivity for a virtual agent. Speech Commun. 51(7), 630–639 (2009)CrossRefGoogle Scholar
  17. 17.
    Camurri, A., Volpe, G.: Communicating expressiveness and affect in multimodal interactive systems. Multimedia 12(1), 43–53 (2005)CrossRefGoogle Scholar
  18. 18.
    Douglas-Cowie, E., Cowie, R., Sneddon, I., Cox, C., Lowry, O., McRorie, M., Martin, J.-C., Devillers, L., Abrilian, S., Batliner, A., Amir, N., Karpouzis, K.: The HUMAINE Database: Addressing the Collection and Annotation of Naturalistic and Induced Emotional Data. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds.) ACII 2007. LNCS, vol. 4738, pp. 488–500. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Roether, C.L., Omlor, L., Christensen, A., Giese, M.A.: Critical features for the perception of emotion from gait. J. Vision 9(6), 15, 1–32 (2009)CrossRefGoogle Scholar
  20. 20.
    Montepare, J.M., Goldstein, S.B.: The identification of emotions from gait information. J. Nonverbal Behav. 11(1), 33–42 (1987)CrossRefGoogle Scholar
  21. 21.
    Janssen, D., et al.: Recognition of emotions in gait patterns by means of artificial neural nets. J. Nonverbal Behav. 32, 79–92 (2008)CrossRefGoogle Scholar
  22. 22.
    Unuma, M., Anjyo, K., Takeuchi, R.: Fourier principles for emotion-based human figure animation. In: SIGGRAPH, Los Angeles, pp. 91–96 (1995)Google Scholar
  23. 23.
    Montepare, J., Koff, E., Zaichik, D., Albert, M.: The use of body movements and gestures as cues to emotions in younger and older adults. J. Nonverbal Behav. 23(2), 133–152 (1999)CrossRefGoogle Scholar
  24. 24.
    Karg, M., Kuhnlenz, K., Buss, M.: Recognition of affect based on gait patterns. IEEE Trans. Sys., Man, Cyber. 40(4), 1050–1061 (2010)CrossRefGoogle Scholar
  25. 25.
    Ma, Y., Paterson, H.M., Pollick, F.E.: A motion-capture library for the study of identity, gender, and emotion perception from biological motion. Behav. Res. Meth., Inst., & Comp. 38, 134–141 (2006)CrossRefGoogle Scholar
  26. 26.
    Bernhardt, D.: Detecting emotions from everyday body movements. Presenccia PhD Sym., Barcelona (2007)Google Scholar
  27. 27.
    Pedregosa, F., et al.: Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetGoogle Scholar
  28. 28.
    Parrot, W.G.: Emotions in social psychology. Philadelphia Press, Philadelphia (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Angelica Lim
    • 1
  • Hiroshi G. Okuno
    • 1
  1. 1.Graduate School of InformaticsKyoto UniversityKyotoJapan

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