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
emotion categorization. Understanding emotions is
essential for many applications, as they are a basic
component of human communication. By investigating
cutting-edge strategies that show promise for enhancing
the precision and effectiveness of emotion recognition, this
study advances the field of emotion classification.