Implementation of Machine Learning based Optimized Speech Emotion Recognition

The ability to identify emotions is critical in several domains, including big data, human-computer interaction, business analytics, and healthcare since emotions are key to human communication and comprehension. However, it becomes challenging when data is presented in multimodal format. The research developments in Speech Emotion Recognition (SER) utilizing Machine Learning (ML) techniques are analyzed in this study. The main goal of this research is to examine how machine learning (ML) algorithms can be combined with nature inspired optimization techniques to recognize and classify human emotions from audio data, particularly voice recordings. The research analyses the difficulties that come with SER, covering problems such accents, background noise, and variations in emotional expressiveness. The study provides a thorough approach that addresses three crucial steps: data preparation, feature extraction, and classification, to address these problems. Numerous methods are implemented for cleaning and preparing audio data during the data preparation stage, including noise reduction, segmentation, and feature extraction. The study also examines the effectiveness of feature extraction techniques for extracting emotional indicators from speech, including Mel-frequency spectral coefficients (MFCCs), Root Mean Square, and deep learning-based embedding.