Emotion detection from multilingual audio using deep analysis

Abstract

Human emotion detection from multiple languages is a very challenging job. In this work, we have used language emotional databases of various languages such as – Ryerson-Audio-Visual database (RAVDESS), Berlin Database (EmoDb) and Italian Database (Emo-Vo) which are in English, German and Italian languages respectively. The proposed model extract MFCC, chroma, Tonnetz, Contrast from the raw audio file, which is further taken as input in the CNN model to identify emotions correctly. We are not using any visual representation of sound only direct from natural sound data. An extensive comparison is made with some of the previous approaches on emotion detection from speech. The experimental result shows that; the proposed model has successfully worked with all the selected databases with higher accuracy. The same also has been tested with the augmented database. We secure 70.46% for RAVDESS, 70.37% Emo-Db and 73.47% for Emo-Vo in the initial database and best model work in the augmented database. However, test with Original test dataset, secured 96.53% in RAVDESS 96.22% in Emo-Db and Emo-Vo 96.11% respectively. Multilingual Emotion detection, a state of art model, has been discussed with an accuracy of 97.89%. The proposed model is a speaker-independent as well as language-independent emotion detection system.