resumo
- In this work, research was done to understand what is needed to build a database to recognise emotions through speech. Some features that can highlight a good success rate for emotion recognition through speech were investigated. Also studied were some characteristics (symptoms) that can be associated with a specific emotional state. On the other hand, we also studied some features that can be used to identify some emotional states. A System Emotion Recognition (SER) was built with SVM, and the binary analysis was compared with a multi-category analysis. The binary analysis achieved an accuracy of 87.5% and the multi-class 42.6%. The parameters Fundamental Frequency-F0, Linear Predictive Coefficients (LPC), and Mel Frequency Cepstral Coeficients (MFCC) were used. The modest accuracy of this work was achieved using only F0, LPC and MFCC features.