WCSE

Dataset for Depression Detection from Speech Emotion Recognition

Abstract— The recognition of internal emotional state of a person plays an important role in several human related fields. Emotions constitute an essential part of our existence as it exerts great influence on the physical and mental health of people. Depression is a common mental disorder. Developments in affect ive sensing technology with focus on acoustic features will potentially bring a change due to depressed patients’ slow, hesitating, monotonous voice as remarkable characteristics. The system is intended for classification of emotions and depression by using speech signals. Both time and frequency domain features will be used in feature vector extraction. In feature extraction, the system will use wavelet transform and MFCC. DenseNet will be used to detect the emotion, classify the type of emotion and then depression. This paper will present about the datasets collected for the system and the experimental results on the dataset using Support Vector Machine.

Index Terms— internal emotional state, feature vector extraction, wavelet transform, MFCC, Densenet, depression

Lwin Lwin Mar, Win Pa Pa, Tin Lay Nwe
UCSY, MYANMAR

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Cite: Lwin Lwin Mar, Win Pa Pa, Tin Lay Nwe, "Dataset for Depression Detection from Speech Emotion Recognition," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering WCSE_2019_SPRING, pp. 101-106, Yangon, Myanmar, February 27-March 1, 2019.