PLOS One
Hybrid CNN-embedding fusion with MFCC-SVM for speech emotion recognition: Random vs actor-wise evaluation on CREMA-D
Speech Emotion Recognition (SER) is an important component of human-centered intelligent systems, yet robust performance remains challenging when speaker identities differ between training and testing. This study presents a protocol-aware and reproducible comparison on the CREMA-D corpus using three pipelines: (i) a classical MFCC-based Support Vector Machine (SVM), (ii) a log-mel Convolutional Neural Network (CNN), and (iii) a lightweight hybrid model that concatenates handcrafted acoustic descriptors with CNN-der …