Implementing machine learning techniques for continuous emotion prediction from uniformly segmented voice recordings - Diemerling, Hannes; Stresemann, Leonie; Braun, Tina; von Oertzen, Timo (Frontiers in Psychology, 2024)
This article presents a novel approach to recognizing emotions from generated 1.5-second audio recordings, utilizing 1510 unique samples sourced from two distinct databases in German and English. Features extracted from these recordings are employed in Deep Neural Networks (DNN), spectrogram features for Convolutional Neural Networks (CNN), and a hybrid model combining both (C-DNN) for emotion prediction. The study underscores challenges in merging datasets due to their heterogeneity, language disparities, and the intricacies of trimming audio recordings. Nevertheless, the results demonstrate that these models achieve an accuracy considerably above random guessing and are on par with human ratings. Given the shortness of the used sample length, this method holds promise for estimating emotional shifts in continuous, longer speech data.