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 $$66.28\%$$66.28%, and at the highest level, the recognition rate of $$78\%$$78% has been obtained, which belongs to the happiness voice signals. The proposed method has $$13.78\%$$13.78% higher average recognition rate and $$28.1\%$$28.1% higher best recognition rate compared to the linear discriminant analysis as well as $$6.58\%$$6.58% higher average recognition rate than the deep neural networks results, both of which have been implemented on the same database.