Emotion Detection in Speech: A CNN Neural Network Approach

Speech emotion recognition is an area of research dedicated to identifying and categorizing emotions expressed through speech. Its purpose is to comprehend and interpret the emotional content conveyed in spoken words, leveraging signal processing techniques, feature extraction algorithms, and machine learning models. The ultimate aim is to apply this knowledge in diverse applications such as human-computer relation, affective computing, and mental health diagnosis. Although the complexity and variability of emotional speech present significant challenges, recent years have witnessed notable progress through the development of advanced algorithms and the utilization of extensive training datasets. Here we proposed a CNN network pattern for speech emotion recognition for the bench mark datasets and got the accuracy of 88% for the convolutional neural network model.