A novel convolutional neural network with gated recurrent unit for automated speech emotion recognition and classification

Automated Speech Emotion Recognition (SER) becomes more popular and has increased applicability. SER concentrates on the automatic identification of the emotional state of a human being using speech signals. It mainly depends upon the in-depth analysis of the speech signal, extracts features containing emotional details from the speech signal, and utilises pattern recognition techniques for emotional state identification. The major problem in automatic SER is to extract discriminate, powerful, and emotional salient features from the acoustical content of speech signals. The proposed model aims to detect and classify three emotional states of speech such as happy, neutral, and sad. The presented model makes use of Convolution neural network–Gated Recurrent unit (CNN-GRU) based feature extraction technique which derives a set of feature vectors. A comprehensive simulation takes place using the Berlin German Database and SJTU Chinese Database which comprises numerous audio files under a collection of different emotion labels.