Speech Emotion Recognition-A Deep Learning Approach

Abstract

Speech emotion recognition is a very popular topic of research among researchers. This research work has implemented a deep learning-based categorization model of emotion produced by speeches based on acoustic data such as Mel Frequency Cepstral Coefficient (MFCC), chromagram, mel spectrogram etc. The developed speech emotion recognition system can recognize emotions like calm, happy, fearful, disgust, angry, neutral, surprised and sad. The Ryerson Audio-Visual Database of Emotional Speech (RAVDESS) and Toronto Emotional Speech Set (TESS) datasets were combined to enlarge our dataset which was used for speech emotion recognition. Specifically, the proposed frame work got an accuracy of 68% while using data augmentation in the RAVDESS dataset. The accuracy increased to 75% while using emotion recognition along with gender recognition in RAVDESS dataset and also by applying data augmentation techniques. Finally, the proposed framework got an accuracy of 89% while using the RAVDESS dataset and TESS datasets and various data augmentation techniques.