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.
This work has the support of Research Centre in Digitalization
and Intelligent Robotics (CEDRI), Instituto Polit´ecnico de Bragan¸ca (IPB), School of
Sciences and Technology-Engineering Department (UTAD). This project is supported
by the European Regional Development Fund (ERDF) through the Regional Operational
Program North 2020, within the scope of Project GreenHealth - Digital strategies
in biological assets to improve well-being and promote green health, Norte -01-0
145-FEDER-000042.
The authors are grateful to the Foundation for Science and Technology (FCT,
Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to
CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/ 2021).
