Abstract:
Sensitive speech, encompassing hate speech, offensive language, and discriminatory
remarks, has become a pressing issue in today's digital landscape. This research focuses on
exploring the efficacy of Convolutional Neural Networks in sensitive speech detection and
classification. CNNs have shown exceptional performance in various natural language
processing tasks, capable of capturing contextual information through learned features. The
study encompasses a comprehensive analysis of audio data preprocessing techniques,
designing and training CNN, Bi-LSTM, and LSTM architectures, and evaluating their
performance using appropriate metrics. Various data augmentation techniques are
employed to enhance the diversity and robustness of the dataset. The CNN, Bi-LSTM, and
LSTM architecture are carefully designed, considering the specific requirements of
sensitive speech detection. Convolutional layers are employed to extract important features
while pooling layers down to sample the extracted representations to capture essential
information efficiently. The trained Bi-LSTM and LSTM models are evaluated using
appropriate metrics, such as accuracy, loss,val_loss, and val_accuracy. Here the highest
accuracy 98.68% achieved by the CNN Base model and the lowest loss 10.43% is also
achieved by the CNN base model. However, the Bi-LSTM and LSTM model shows the
comparatively lowest model accuracy 68.69% and 48.09% for my dataset. The results
demonstrate the effectiveness of CNNs in sensitive speech detection and classification
tasks, showcasing their ability to accurately identify sensitive speech and non-sensitive
speech. The research contributes to the development of robust tools and frameworks for
content moderation in online platforms. Future work involves addressing these limitations
and exploring advanced techniques, and multi-language attention mechanisms, to further
improve the performance of sensitive speech detection systems. Ultimately, this research
serves as a foundation for promoting respectful and inclusive digital communities by
combating the detrimental impact of sensitive speech.
