DSSCNet: A Transfer Learning Framework for Cross-Corpus Dysarthric Speech Severity Classification

Arnab Kumar Roy; Hemant Kumar Kathania; Paban Sapkota; Sudarsana Reddy Kadiri; Shrikanth Narayanan

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arXiv · 2606.22178

DSSCNet: A Transfer Learning Framework for Cross-Corpus Dysarthric Speech Severity Classification

Arnab Kumar Roy·Hemant Kumar Kathania·Paban Sapkota·Sudarsana Reddy Kadiri·Shrikanth Narayanan

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Abstract

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.

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BibTeX RIS

Arnab Kumar Roy, Hemant Kumar Kathania, Paban Sapkota, Sudarsana Reddy Kadiri, Shrikanth Narayanan. 2026-06-20. DSSCNet: A Transfer Learning Framework for Cross-Corpus Dysarthric Speech Severity Classification. https://arxiv.org/abs/2606.22178

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