Comprehensive Speech Emotion Recognition System Employing Multi-Layer Perceptron (MLP) Classifier and libRosa Feature Extraction

The development of a machine learning model for emotion detection in speech is a significant advancement, catering to the growing demand for personalization in daily experiences. This technology, leveraging libraries like librosa, sound file, and sklearn, employs an MLP Classifier to accurately assess emotions within sound files. Its potential applications span across industries, notably in marketing, where tailored product recommendations based on an individual's mood can be offered The process involves data loading, feature extraction, and dataset partitioning for training and testing. By recognizing emotional patterns, this innovation has the capacity to transform how businesses engage with consumers, providing highly personalized services and recommendations aligned with their emotional states. Emotion detection can elevate user experiences, rendering a more adaptive and responsive. As technology evolves, the integration of emotion recognition systems is poised to redefine personalized interactions across diverse sectors, shaping the future of customer-centric services.