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Advisor(s)
Abstract(s)
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.
Description
Keywords
Emotional state Speech SVM
Citation
Teixeira, Felipe L.; Teixeira, João Paulo; Soares, Salviano F.P.; Abreu, J.L.Pio (2022). F0, LPC, and MFCC analysis for emotion recognition based on speech. In Second International Conference, OL2A 2022. Bragança. 1754, p.389-404
Publisher
Springer Nature