A Symphony of Emotions: Exploring Machine Learning Techniques for Speech Emotion Recognition
Dr. Narendra Kumar1, Faculty CSE, HMRITM,
Ritika Bhardwaj2, Gautam Gupta3, Tushar Shokeen4, Sachin Chauhan5
B. Tech 4th Year, Dept. of Computer Science and Engineering, HMR Institute of Technology & Management, Delhi, India
ABSTRACT
Speech Emotion Recognition (SER) has emerged as a captivating field of research, enabling machines to comprehend and interpret human emotions conveyed through speech signals. This paper presents a comprehensive investigation into the application of machine learning techniques for speech emotion recognition. By leveraging a diverse dataset comprising of emotional speech samples, our study aims to develop an accurate and robust system capable of recognizing and categorizing emotions in real-time speech data. The proposed methodology utilizes a combination of feature extraction techniques and machine learning algorithms to extract relevant acoustic features from the speech signals. These features encompass a wide range of spectral, prosodic, and temporal attributes, capturing various aspects of emotional expression. The extracted features are then employed to train and evaluate multiple machine learning models, including Support Vector Machines (SVM), Random Forests, and Deep Neural Networks (DNN), to determine their effectiveness in recognizing and classifying emotions. To assess the performance of the developed system, extensive experiments were conducted on a benchmark dataset, containing a diverse set of emotional expressions. The results demonstrated the superiority of the deep neural network approach, achieving an accuracy rate of 85%, outperforming the other evaluated models. Furthermore, the proposed system exhibited robustness and generalizability, demonstrating consistent performance across different speakers and emotional contexts. In addition to performance evaluation, this research paper discusses the potential applications and implications of Speech Emotion Recognition Systems in various domains, such as affective computing, human-computer interaction, and psychological research. The findings highlight the importance of accurate emotion recognition in enhancing human-machine interactions, personalizing user experiences, and improving emotional well-being.
In conclusion, this research contributes to the advancement of speech emotion recognition by presenting an effective methodology that combines feature extraction techniques with state-of-the-art machine learning models. The achieved results underscore the potential of machine learning approaches in accurately decoding and categorizing emotions from speech signals. The proposed system holds promise for real-world applications in areas where emotion understanding and response play a crucial role.
Keywords: Speech Emotion Recognition, Support Vector Machine, Random Forest, Deep Neural Network
