Gender based Real Time Vocal Emotion Detection - Anchan, Anusha; G. R., Manasa; Pinto, Joylin Priya (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Emotion Detection (ED) is recognizing the emotional facet of speech regardless of its semantics. Although human beings are able to perform this task efficaciously, automatically conducting it, utilizing programming devices or techniques is still a subject of research. This work proposes, several classifier algorithms have been implemented and compared based on the accuracy and emotion category. MLP, SVM, CNN and DNN with LSTM layer have been trained. At first the classifiers are training with initial datasets. The datasets that are being used are CREMA-D, TESS, SAVEE and RAVDESS. Subsequently, with pre-processing of data and data augmentations using four parameters namely Noise, Stretch, Speed and Pitch. Zero Crossed Rate, Energy – Root Mean Square and MFCC are extracted from the data. Models have witnessed improvement in accuracy under the post processing. Models thrived and succeeded in achieving accuracy of 66.61% (MLP), 53% (SVM), 88.35% (CNN) and 91.30% (DNN). This work also proposes a Real time Speech Detection System acts as artificial intelligence which classifies emotions by analyzing the audio files via the trained model based on gender.