7 papers
Quantifying Dimensional Independence in Speech: An Information-Theoretic Framework for Disentangled Representation Learning
Bipasha Kashyap, Björn W. Schuller, Pubudu N. Pathirana
Speech signals encode emotional, linguistic, and pathological information within a shared acoustic channel; however, disentanglement is typically assessed indirectly through downst…
Geometric Analysis of Speech Representation Spaces: Topological Disentanglement and Confound Detection
Bipasha Kashyap, Pubudu N. Pathirana
Speech-based clinical tools are increasingly deployed in multilingual settings, yet whether pathological speech markers remain geometrically separable from accent variation remains…
Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications
Ha Tran, Bipasha Kashyap, Pubudu N. Pathirana
Speech-based machine learning systems are sensitive to noise, complicating reliable deployment in emotion recognition and voice pathology detection. We evaluate the robustness of a…
Leveraging Vision Transformers for Enhanced Classification of Emotions using ECG Signals
Pubudu L. Indrasiri, Bipasha Kashyap, Pubudu N. Pathirana
Biomedical signals provide insights into various conditions affecting the human body. Beyond diagnostic capabilities, these signals offer a deeper understanding of how specific org…
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
Kanishka Ranaweera, Azadeh Ghari Neiat, Xiao Liu +2
Federated learning (FL) has emerged as a promising paradigm in machine learning, enabling collaborative model training across decentralized devices without the need for raw data sh…
Quantum Approaches for Dysphonia Assessment in Small Speech Datasets
Ha Tran, Bipasha Kashyap, Pubudu N. Pathirana
Dysphonia, a prevalent medical condition, leads to voice loss, hoarseness, or speech interruptions. To assess it, researchers have been investigating various machine learning techn…