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Deep learning models can detect voice pathologies linked to neurological disorders. A 2D convolutional neural network (CNN) demonstrated better generalization for early voice pathology diagnosis compared to 1D CNN.

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Speech Science

Background:

  • Neurological illnesses like Parkinson's disease, multiple sclerosis, myasthenia gravis, and ALS can manifest motor and non-motor symptoms affecting speech.
  • Voice disorders stem from disruptions in neural pathways controlling speech production, impacting systems like the corticospinal tract, cerebellum, basal ganglia, and motoneurons.
  • Voice pathology detection technologies offer potential for early assessment and diagnosis of voice irregularities.

Purpose of the Study:

  • To develop and evaluate deep-learning computational models for detecting voice pathologies.
  • To compare the performance of 1-dimensional convolutional neural network (1D CNN) and 2-dimensional convolutional neural network (2D CNN) for voice pathology detection.
  • To assess the models' ability to diagnose voice pathologies caused by neurological conditions or other factors.

Main Methods:

  • Utilized voice recordings of sustained vowel /a/ from the German corpus Saarbruecken Voice Database (SVD).
  • Applied data preprocessing techniques including padding and segmentation to voice signals.
  • Implemented 1D CNN and 2D CNN models, incorporating convolutional layers and Mel-frequency cepstral coefficient (MFCC) feature extraction.

Main Results:

  • The 1D CNN achieved a maximum accuracy of 93.11% on test data but exhibited overfitting during training.
  • The 2D CNN demonstrated better data generalization with lower training and validation loss, achieving 84.17% accuracy on test data.
  • The 2D CNN model outperformed state-of-the-art studies, suggesting that models trained on handcrafted features are superior for speech processing in this context.

Conclusions:

  • Deep learning models, particularly the 2D CNN, show promise for the simultaneous detection of voice pathologies from neurological disorders.
  • The 2D CNN's superior generalization indicates its potential for reliable early diagnosis of voice pathologies.
  • Handcrafted features combined with deep learning, as seen in the 2D CNN approach, may be more effective for speech processing tasks than end-to-end feature extraction.