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 devia...

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Bibliographic Details
Published in:International journal of speech technology 2017-06, Vol.20 (2), p.239-246
Main Authors: Noroozi, Fatemeh, Sapiński, Tomasz, Kamińska, Dorota, Anbarjafari, Gholamreza
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Decision trees
Deep learning
Discriminant analysis
Emotion recognition
Emotions
Engineering
Formants
Harmonics
Neural networks
Pitch
Prosodic features
Signal to noise ratio
Signal,Image and Speech Processing
Social Sciences
Voice recognition
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Summary: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 % , and at the highest level, the recognition rate of 78 % has been obtained, which belongs to the happiness voice signals. The proposed method has 13.78 % higher average recognition rate and 28.1 % higher best recognition rate compared to the linear discriminant analysis as well as 6.58 % higher average recognition rate than the deep neural networks results, both of which have been implemented on the same database.
ISSN:1381-2416
1572-8110
DOI:10.1007/s10772-017-9396-2