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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A novel Swin transformer based framework for speech recognition for dysarthria.

Rabbia Mahum1, Ismaila Ganiyu2, Lotfi Hidri2

  • 1Department of Computer Science, University of Engineering and Technology Taxila, Taxila, 47050, Pakistan. rabbia.mahum@uettaxila.edu.pk.

Scientific Reports
|June 16, 2025
PubMed
Summary

This study introduces DSR-Swinoid, a novel Swin transformer model for accurate dysarthria detection. The enhanced model achieves 98.66% accuracy, significantly improving upon existing methods for speech disorder identification.

Keywords:
AI in healthcareDysarthriaGlobal featuresLocal features

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Area of Science:

  • Speech-Language Pathology
  • Artificial Intelligence
  • Biomedical Signal Processing

Background:

  • Dysarthria, a speech disorder common in neurological conditions like Parkinson's disease and stroke, requires timely detection.
  • Existing machine learning methods for dysarthria detection suffer from high false positive rates due to speech variability and background noise.

Purpose of the Study:

  • To develop an improved Swin transformer-based model, DSR-Swinoid, for more accurate dysarthria speech detection.
  • To enhance the Swin transformer's local feature extraction capabilities for better identification of speech impairments.

Main Methods:

  • Speech signals were converted into mel-spectrograms to capture voice patterns.
  • A novel DSR-Swinoid model was developed, integrating four modules (NLF, convolutional patch concatenation, MP, MVB) to enhance local feature learning in the Swin transformer.

Main Results:

  • The DSR-Swinoid model achieved a high accuracy of 98.66% in detecting dysarthria.
  • The proposed model demonstrated superior performance compared to existing methods.

Conclusions:

  • The DSR-Swinoid model offers a significant advancement in the accurate detection of dysarthria.
  • The enhanced Swin transformer architecture effectively addresses limitations of previous methods, paving the way for improved clinical diagnostics.