Abstract:
Speech classification is a crucial task in various applications, such as speech recognition
and speech analysis. CNNs have shown remarkable success in speech classification tasks
for different languages. This research paper aims to enhance Bengali speech classification
performance by applying CNNs to a large-scale Bengali speech dataset. The proposed
approach includes preprocessing techniques, CNN architecture design, and training
strategies specifically tailored for Bengali speech data. Experimental results demonstrate
significant improvements in Bengali speech classification accuracy, paving the way for
enhanced speech-related applications in the Bengali language. Training strategies are
carefully devised, selecting optimization algorithms, learning rate schedules, and batch
sizes to maximize classification accuracy of 95.99%. Extensive experiments on the
prepared Bengali speech dataset collected from several Facebook posts demonstrate
significant improvements in classification accuracy compared to existing methods. The
proposed CNN model achieves high accuracy indicating its efficacy in accurately
classifying Bengali speech samples. This research contributes to advancing Bengali
speech classification using CNNs and showcases the potential of CNNs in processing and
analyzing Bengali speech data. This research paper concludes with a thorough
investigation of improving Bengali speech categorization using convolutional neural
networks. The proposed approach, including dataset preparation, tailored CNN
architecture, and optimized training strategies, leads to significant improvements in
Bengali speech classification accuracy. The findings highlight the potential impact of
CNNs in advancing speech-related applications in the Bengali language.
