Speech emotion recognition in Persian based on stacked autoencoder by comparing local and global features

Among the barriers to establishing effective human-machine interactions is the machines’ inability to properly distinguish emotions from the human voice. The Speech Emotion Recognition (SER) systems have emerged to tackle this limitation. The accuracy of these systems depends on different factors such as the quantity and the types of emotions included in the database, feature extraction process including local and global features, feature selection method, and the type of classifier. This study presents a methodology for speech emotion recognition using an autoencoder neural network. It is shown that using a digit-level stacked autoencoder can be suitable for digit classification. The speech emotion recognition is done using the Persian emotional speech database (Persian ESD), which includes six emotional states: Happiness, Sadness, Fear, Disgust, Anger, and Neutral. Moreover, the popular, widely-used Berlin Emotional database (EMO-DB) is used to evaluate the effectiveness of the proposed approach. The experimental results show that the proposed method has significantly improved recognition accuracy.