Dorota Kamińska

Vocal-based emotion recognition using random forests and decision tree

This paper proposes a new vocal-based emotion recognition method using random forests, where pairs of the features on the whole speech signal, namely, pitch, intensity, the first four formants, the first four formants bandwidths, mean autocorrelation, mean noise-to-harmonics ratio and standard deviation, are used in order to recognize the emotional state of a speaker. The proposed technique adopts random forests to represent the speech signals, along with the decision-trees approach, in order to classify them into different categories. The emotions are broadly categorised into the six groups, which are happiness, fear, sadness, neutral, surprise, and disgust. The Surrey Audio-Visual Expressed Emotion database is used. According to the experimental results using leave-one-out cross-validation, by means of combining the most significant prosodic features, the proposed method has an average recognition rate of

Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification

66.28\%

Audiovisual emotion recognition in wild

66.28%, and at the highest level, the recognition rate of

Virtual reality as a tool in mechatronics education

78\%

Comparison of perceptual features efficiency for automatic identification of emotional states from speech signal

78% has been obtained, which belongs to the happiness voice signals. The proposed method has