Acoustic feature-based emotion recognition and curing using ensemble learning and CNN

Emotion recognition and understanding plays a crucial role in various domains, including healthcare, human-computer interaction, and mental well-being. In this context, this paper proposes a methodology for recognizing and curing emotions using acoustic features and machine learning algorithms. The approach involves extracting acoustic features from the signals using diverse signal processing techniques. These features are then utilized as inputs for machine learning and deep learning algorithms, including the Random Forest classifier, XG Boost classifier, Convolutional Neural Network (CNN), and an ensemble algorithm. The ensemble algorithm combines Random Forest and XG Boost as base classifiers, with the Naïve Bayes algorithm serving as the meta classifier. We also propose a novel model that generates personalized curing strategies for individuals based on emotion recognition, so they can keep their emotional state positive. With the help of an ensemble learning model the proposed model achieved an emotion recognition accuracy of 92 % by combining three publicly available datasets containing emotional speech recordings. In the neutral and positive emotion classifications, the Receiver Operating Characteristic curve (ROC) had a 98 % accuracy rate while negative emotion classifications had a 91 % true positive rate. The effectiveness of the proposed curing methodology model has also been demonstrated by conducting experiments on a group of individuals and comparing the results with a state-of-the-art Generative Pre-Trained Transformer-3 (GPT-3) and ChatGPT, it was inferred that 89.35 % of the test group preferred the responses of the proposed curing model, over the GPT models The results of our experiments show that our proposed methodology can significantly boost the emotional state of an individual, thereby highlighting its potential for use in clinical settings.