Enhancing Speech Emotion Recognition Through Advanced Feature Extraction and Deep Learning: A Study Using the RAVDESS Dataset

This study focuses on the significance of speech emotion recognition (SER) in facilitating the ability of machines to recognize and comprehend human emotions expressed through speech. This study introduces an innovative method for SER that incorporates sophisticated feature extraction techniques and deep learning approaches. The main objective is to improve the accuracy of identification, reduce processing requirements, and enable real-time application. Our suggested methodology utilizes the RAVDESS dataset and incorporates Mel-frequency cepstral coefficient (MFCC) characteristics that are processed by a convolutional neural network (CNN) model. Our methodology has higher performance in comparison to baseline methods, as evidenced by extensive experimentation and analysis. The CNN model demonstrates successful generation of feature maps for the time series data, resulting in improved extraction and understanding of MFCC features. The effectiveness and robustness of our suggested approach are demonstrated by evaluation measures such as recognition accuracy, computing time, and real-time applicability. The superiority of our technique is further validated through comparative assessments with existing approaches, which confirm its genericity, accuracy, and dependability in various SER scenarios. The study's ROC diagrams offer visual substantiation of the model's efficacy in accurately classifying emotions across diverse emotion categories. This study not only makes a valuable contribution to the progress of SER technology but also establishes the foundation for future investigations in this domain. The findings obtained from our research provide a foundation for the advancement of more advanced and efficient SER systems, which have wide-ranging ramifications in several fields such as human-computer interaction, sentiment analysis, mental health diagnostics, and other related areas.