BAH: Beyond Acoustic Handcrafted features for speech emotion recognition in Portuguese - Guder, Larissa; Dopke, Luan; Kaiser, Marcos; Griebler, Dalvan; Meneguzzi, Felipe (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the WebDO - 10.5753/webmedia.2025.16129, 2025)
It is through affective computing that we have the integration of human feelings and computing applications. One affective computing task is Speech Emotion Recognition (SER), which identifies emotions from spoken audio. Even though emotion is a universal aspect of human experience, each culture and language has different ways to express and understand emotions. So, when designing models for SER, it is common to focus on a single language. In this work, we explore VERBO, a Brazilian Portuguese dataset for categorical emotion recognition. Our main objective is to define the best way to extract acoustic features to train a classifier for SER.We compare 18 different methods to generate audio representations, grouped by handcrafted features and audio embeddings. The best representation for VERBO is TRILL embeddings, and with an SVM classifier, we achieved 92% accuracy in VERBO. As far as we know, this was the state of the art for this dataset.