Speech-based emotion recognition using a hybrid RNN-CNN network

Speech emotion recognition is probably among the most exciting and dynamic areas of modern research focused on speech signals analysis, which allows estimating and classifying speakers' rich spectrum of emotions. The following paper aims to develop a novel deep learning (DL)-based model for detecting speech emotion variation to overcome several weaknesses of the existing intelligent data-driven approaches. A new architecture for a DL network, referred to as the RNN-CNN, is proposed and applied in this paper to perform the SER task by operating directly on raw speech signals. Specifically, the challenge was effectively combining an initial convolution layer with a wide kernel as an efficient way to address and mitigate the problems caused by noise found in raw speech signals. In this experimental analysis, the 3 databases used to evaluate the proposed RNN-CNN model are RML, RAVDESS, and SAVEE. The effectiveness of such methodologies can be detected with remarkable efficacy, whose improved accuracy rates depict contrasting trends from those findings of the previous works analyzed through respective datasets. This assessment has validated the robust performance and applicability of the suggested models for diverse speech databases and underlined their potential in further speech-based emotion recognition.