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Automatic speech based emotion recognition using paralinguistics features

Hook, J. ; Noroozi, F. ; Toygar, O. ; Anbarjafari, G.

Bulletin of the Polish Academy of Sciences. Technical sciences, 2019-01, Vol.67 (3), p.479-488 [Periódico revisado por pares]

Warsaw: Polish Academy of Sciences

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  • Título:
    Automatic speech based emotion recognition using paralinguistics features
  • Autor: Hook, J. ; Noroozi, F. ; Toygar, O. ; Anbarjafari, G.
  • Assuntos: Affective computing ; Emotion recognition ; Emotions ; Females ; machine learning ; Males ; random forests ; speech emotion recognition ; Speech recognition ; Support vector machines
  • É parte de: Bulletin of the Polish Academy of Sciences. Technical sciences, 2019-01, Vol.67 (3), p.479-488
  • Descrição: Affective computing studies and develops systems capable of detecting humans affects. The search for universal well-performing features for speech-based emotion recognition is ongoing. In this paper, a small set of features with support vector machines as the classifier is evaluated on Surrey Audio-Visual Expressed Emotion database, Berlin Database of Emotional Speech, Polish Emotional Speech database and Serbian emotional speech database. It is shown that a set of 87 features can offer results on-par with state-of-the-art, yielding 80.21, 88.6, 75.42 and 93.41% average emotion recognition rate, respectively. In addition, an experiment is conducted to explore the significance of gender in emotion recognition using random forests. Two models, trained on the first and second database, respectively, and four speakers were used to determine the effects. It is seen that the feature set used in this work performs well for both male and female speakers, yielding approximately 27% average emotion recognition in both models. In addition, the emotions for female speakers were recognized 18% of the time in the first model and 29% in the second. A similar effect is seen with male speakers: the first model yields 36%, the second 28% a verage emotion recognition rate. This illustrates the relationship between the constitution of training data and emotion recognition accuracy.
  • Editor: Warsaw: Polish Academy of Sciences
  • Idioma: Polonês;Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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