Abstract:
This research investigates the classification of Bengali regional accents using speech data
and machine learning techniques. Accurate recognition of regional accents plays a pivotal
role in improving natural language processing systems in linguistically diverse regions
such as Bangladesh. Speech data was collected from various regions, including 580 audio
samples from Chandpur, 535 from General Bengali, 484 from Bogura, 456 from
Chittagong, 420 from Sylhet, 413 from Barishal, and 28 from other areas. The dataset was
preprocessed to extract key speech features, which were then used as inputs for machine
learning models.Four machine learning algorithms were applied and evaluated: Random
Forest, Decision Tree, K-Nearest Neighbors, and Logistic Regression. Among these, the
Random Forest model demonstrated the highest accuracy, achieving 98.12%. The Decision
Tree model followed with 87.67%, while K-Nearest Neighbors and Logistic Regression
attained 75.17% and 65.92%, respectively. These findings highlight the superiority of
ensemble methods such as Random Forest in managing complex and diverse datasets. The
study also addresses the challenges in accent classification, particularly the variability in
speech patterns and the limited data availability for less-represented regions. The
inclusion of the "others" category further underlines the necessity of more comprehensive
and balanced datasets to improve model generalizability. This work significantly
contributes to the fields of computational linguistics and speech recognition, showcasing
the effectiveness of machine learning in accent classification. The exceptional performance
of the Random Forest model underscores its potential for real-world applications, such as
automated transcription, accent-based recommendations, and language learning systems.
Future work may focus on enhancing the dataset and leveraging advanced deep learning
techniques to further improve accuracy and performance.
