A Machine Learning Approach for Emotion Classification in Bengali Speech

In this research work, we have presented a machine learning strategy for Bengali speech emotion categorization with a focus on Mel-frequency cepstral coefficients (MFCC) as features. The commonly utilized method of MFCC in speech processing has proved effective in obtaining crucial phoneme-specific data. This paper analyzes the efficacy of four machine learning algorithms: Random Forest, XGBoost, CatBoost, and Gradient Boosting, and tackles the paucity of research on emotion categorization in non-English languages, particularly Bengali. With CatBoost obtaining the greatest accuracy of 82.85%, Gradient Boosting coming in second with 81.19%, XGBoost coming in third with 80.03%, and Random Forest coming in fourth with 80.01%, experimental evaluation shows encouraging outcomes. MFCC features improve classification precision and offer insightful information on the distinctive qualities of emotions expressed in Bengali speech. By demonstrating how well MFCC characteristics can identify emotions in Bengali speech, this study advances the field of emotion classification. Future research can investigate more sophisticated feature extraction methods, look into how temporal dynamics are incorporated into emotion classification models, and investigate practical uses for emotion detection systems in Bengali speech. This study advances our knowledge of emotion classification and paves the way for more effective emotion identification systems in Bengali speech by utilizing MFCC and machine learning techniques. Our work addresses the need for thorough and efficient techniques to recognize and classify emotions in speech signals in the context of essential for many applications, as they are a basic component of human communication. By investigating the precision and effectiveness of emotion recognition, this study advances the field of emotion classification.