Classification of Human Emotion from Speech Recognition Using Deep Learning
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Abstract
Human emotions are complex mental processes that respond to surrounding stimulators. It is a mechanism that allows humans to adjust themselves and express their emotions in various situations. In a particular situation, humans can manifest their emotions diversely. Therefore, it is difficult to catch and understand the actual emotions. Predicting the interlocutors’ emotions help to decide proper actions for specific situations such as for treating patients with depression or those who need psychotherapy. This study develops deep learning models to classify human emotions by using human speech. Then, humans’ voices are classified based on five emotional types including normal emotion, anger, surprise, happiness, and sadness. The objectives of this study are to 1) compare the performances of two classifying models i.e., Convolution Neuron Networks (CNN), and Long Short-Term Memory (LSTM), and 2) propose the most appropriate model for classifying humans’ emotions from speech recognition. It reveals that classification results generated by LSTM outperform CNN. With LSTM, there are four classes to recognize humans’ speech emotions such as normal, angry, surprised, happy, and sad.
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