  In the era of advanced artificial intelligence and human-computer
interaction, identifying emotions in spoken language is paramount. This
research explores the integration of deep learning techniques in speech emotion
recognition, offering a comprehensive solution to the challenges associated
with speaker diarization and emotion identification. It introduces a framework
that combines a pre-existing speaker diarization pipeline and an emotion
identification model built on a Convolutional Neural Network (CNN) to achieve
higher precision. The proposed model was trained on data from five speech
emotion datasets, namely, RAVDESS, CREMA-D, SAVEE, TESS, and Movie Clips, out
of which the latter is a speech emotion dataset created specifically for this
research. The features extracted from each sample include Mel Frequency
Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), Root Mean Square (RMS),
and various data augmentation algorithms like pitch, noise, stretch, and shift.
This feature extraction approach aims to enhance prediction accuracy while
reducing computational complexity. The proposed model yields an unweighted
accuracy of 63%, demonstrating remarkable efficiency in accurately identifying
emotional states within speech signals.
