Emotion Recognition in Speech: A Natural Language Processing Perspective

Speech emotion recognition is the process of accurately interpreting an individual's emotion from their speech. This paper introduces a python-based approach using natural language processing techniques for real-time emotion recognition in speech. The system utilizes a combination of feature extraction, acoustic modeling, and sentiment analysis to accurately identify and categorize different emotions expressed in spoken language. The feature extraction process involves extracting relevant acoustic features such as from the speech signal. These features are then used to train machine learning algorithms to classify emotions. sentiment analysis techniques are incorporated to capture additional emotional cues present in the text of the speech. The system demonstrates promising results in real-time emotion recognition, achieving high classification accuracies for a range of emotions. The system's efficacy and productivity are further enhanced by leveraging popular Python libraries such as NLTK, Scikit-learn, and Keras for natural language processing and machine learning tasks. The ultimate goal is to enhance emotion recognition accuracy significantly, with observed improvements ranging from 95% to 98%. Through this work experimental evaluation of the system on various emotion datasets shows its capability to recognize emotions in real-time speech with real- world applications