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Area of Science:

  • Otolaryngology
  • Speech-Language Pathology
  • Artificial Intelligence

Background:

  • Acoustic analysis of voice signals offers potential for early detection and diagnosis of voice disorders.
  • Image-based, neural network approaches may effectively analyze acoustic signals for voice disorder classification.

Purpose of the Study:

  • To provide proof-of-concept for deep learning neural network analysis in decoding human phonation data.
  • To accurately and efficiently differentiate between normal and disordered voices using AI.

Main Methods:

  • Acoustic recordings from 80 participants (10 healthy, 70 with voice disorders) were analyzed.
  • Acoustic signals were converted into spectrograms and used to train a convolutional neural network.
  • Models were trained and validated using 10-fold cross-validation for binary classification tasks.

Main Results:

  • Average classification accuracies ranged from 58% to 90% across seven voice disorder categories.
  • High accuracy was observed in classifying adductor spasmodic dysphonia, unilateral vocal fold paralysis, vocal fold polyp, polypoid corditis, and recurrent respiratory papillomatosis.
  • Findings align with previous studies on deep neural networks for voice disorder classification.

Conclusions:

  • Preliminary results indicate deep learning holds promise for clinical voice disorder detection and diagnosis.
  • Further research with larger sample sizes is recommended to optimize current models.
  • AI-driven acoustic analysis may significantly advance the field of voice disorder diagnostics.