Unraveling Emotional Dimensions in Brazilian Portuguese Speech through Deep Learning - Augusto, Henrique Tibério B. V.; Gonçalves, Vinícius P.; Canedo, Edna Dias; Meneguette, Rodolfo; Pessin, Gustavo; R. Filho, Geraldo Pereira (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and Learning, 2024)
Speech is often our first form of communication and expression of emotions. Speech Emotion Recognition is a complex problem, as emotional expression depends on spoken language, dialect, accent, and the cultural background of individuals. The intensity of this emotion can affect our perception and lead us to interpret information inappropriately, with potential applications in various fields such as: patient monitoring, security, commercial systems, and entertainment. This work performed a Machine Learning task using both Machine Learning and Deep Learning to infer the intensity of emotions in Portuguese speech, employing Domain Fusion with two distinct databases. To do so, an Autoencoder was created to extract features, and then we trained a supervised model to classify the intensities into four classes: (i) weak; (ii) moderate; (iii) high; and (iv) peak intensity. The results indicate the possibility of inferring intensity, although the dataset is limited, even when combining two datasets. Two experimental scenarios were carried out, with analogous architectures, varying the dimensionality of representative features used as input for the models. Additionally, observing the performance metrics, it was possible to note the recurrence of the same class (high) with the lowest variation of F1-Score between both experiments, which raises questions for further studies, while the most distant classes (weak and peak) had the best performance for both experiments.