Speech Recognition and Emotion Detection using Machine Learning approaches on Ravdess Dataset

Speech analysis can be employed in real-time to detect emotions using machine learning. Emotions can be identified by analysing features such as pitch, intensity, and spectral patterns. Various techniques like as neural networks or support vector machines, can be utilised to classify these emotions. It has applications in the fields of psychology, customer service, and computer interface. This work specifically examined the correlation between emotions and speaking. The classification model was trained on the RAVDESS dataset using a combination of deep neural network, Support Vector Machine, and Multi-layer perceptron. The paradigm distinguishes between eight distinct emotions: Neutral, Happy, Angry, Sad, Calm, Surprise, Disgust, and Afraid. The paradigm distinguishes between 8 distinct emotions: Neutral, Calm, Happy, Sad, Angry, Afraid, Disgust, and Surprise. This concept has the potential to transform human-computer interaction by enabling machines to comprehend and reciprocate human emotions. Improved data collection and analysis, uncovering hidden insights in healthcare, enhancing customer service, and developing technology that accurately captures the intricacies of human experience.