Arnab Roy

Arnab Kumar Roy, Hemant Kumar Kathania, Adhitiya Sharma et al.

The human face is a silent communicator, expressing emotions and thoughts through its facial expressions. With the advancements in computer vision in recent years, facial emotion recognition technology has made significant strides, enabling machines to decode the intricacies of facial cues. In this work, we propose ResEmoteNet, a novel deep learning architecture for facial emotion recognition designed with the combination of Convolutional, Squeeze-Excitation (SE) and Residual Networks. The inclusion of SE block selectively focuses on the important features of the human face, enhances the feature representation and suppresses the less relevant ones. This helps in reducing the loss and enhancing the overall model performance. We also integrate the SE block with three residual blocks that help in learning more complex representation of the data through deeper layers. We evaluated ResEmoteNet on four open-source databases: FER2013, RAF-DB, AffectNet-7 and ExpW, achieving accuracies of 79.79%, 94.76%, 72.39% and 75.67% respectively. The proposed network outperforms state-of-the-art models across all four databases. The source code for ResEmoteNet is available at https://github.com/ArnabKumarRoy02/ResEmoteNet.

Arnab Kumar Roy, Hemant Kumar Kathania, Adhitiya Sharma

Facial Emotion Recognition (FER) plays a crucial role in computer vision, with significant applications in human-computer interaction, affective computing, and areas such as mental health monitoring and personalized learning environments. However, a major challenge in FER task is the class imbalance commonly found in available datasets, which can hinder both model performance and generalization. In this paper, we tackle the issue of data imbalance by incorporating synthetic data augmentation and leveraging the ResEmoteNet model to enhance the overall performance on facial emotion recognition task. We employed Stable Diffusion 2 and Stable Diffusion 3 Medium models to generate synthetic facial emotion data, augmenting the training sets of the FER2013 and RAF-DB benchmark datasets. Training ResEmoteNet with these augmented datasets resulted in substantial performance improvements, achieving accuracies of 96.47% on FER2013 and 99.23% on RAF-DB. These findings shows an absolute improvement of 16.68% in FER2013, 4.47% in RAF-DB and highlight the efficacy of synthetic data augmentation in strengthening FER models and underscore the potential of advanced generative models in FER research and applications. The source code for ResEmoteNet is available at https://github.com/ArnabKumarRoy02/ResEmoteNet

Arnab Kumar Roy, Hemant Kumar Kathania, Paban Sapkota et al.

Dysarthric speech severity classification is challenging due to speaker variability, class imbalance, and limited datasets. This study introduces DSSCNet, a deep learning model that employs transfer learning and multi-corpus learning to enhance speaker-independent classification. By pre-training on one dysarthric speech corpus and fine-tuning on another, DSSCNet achieves improved feature extraction and cross-corpus generalization. Experimental results demonstrate that DSSCNet outperforms state-of-the-art models for speaker-independent severity classification, achieving 75.80\% accuracy on TORGO and 68.25\% on UA-Speech, significantly reducing misclassification errors. The findings confirm that leveraging knowledge transfer between datasets improves model robustness, making DSSCNet well-suited for automated dysarthria assessment. This research contributes to the development of more effective assistive speech technologies for individuals with speech impairments.