Speech Emotion Recognition

Speech Emotion Recognition

R. Ramcharan, S. Ramesh, D. Ramya, J.Prathyusha, P.Ranadheer, V.Ranjith, Dr G Hariharan

School of Engineering

MallaReddy University

Abstract: This abstract provides a concise overview of the problem, research method, and design in the context of speech emotion recognition using the Librosa library and MLP classifier.

The problem addressed in this research is the accurate recognition and classification of emotions conveyed through speech signals. The research method involves utilizing the Librosa library for feature extraction and the MLP classifier for emotion classification. The design of the study involves applying pre-processing steps and data augmentation techniques to enhance the quality and diversity of the training data.

The objective of the research is to develop a precise and accurate speech emotion recognition system. By leveraging the Librosa library and MLP classifier, the study aims to improve the understanding and analysis of human emotions from speech signals.

The findings contribute to the advancement of speech emotion recognition and have practical implications in affective computing, in achieving precise and accurate speech emotion recognition using the Librosa library and MLP classifier. The motivation behind this project stems from the need for robust and efficient emotion recognition systems. Despite advancements in the field, challenges remain in handling variations in speech patterns and mitigating the impact of background noise. The proposed system leverages the Librosa library for feature extraction, capturing essential acoustic cues, and employs the MLP classifier for effective emotion classification.