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Auditory Feature Extraction Approach for Robust Pathological Voice Recognition.

Youssef Zouhir1, Mohamed Zarka1, Lilia El Amraoui2

  • 1Research Laboratory Smart Electricity & ICT, SE&ICT Lab, LR18ES44, National Engineering School of Carthage, University of Carthage, Tunis, Tunisia.

Journal of Voice : Official Journal of the Voice Foundation
|January 15, 2026
PubMed
Summary
This summary is machine-generated.

A novel Auditory Feature Extraction (AFE) method using a Gammachirp FilterBank significantly improves pathological voice recognition. This approach achieves high accuracy in classifying voice disorders, outperforming existing methods for better clinical screening.

Keywords:
AFEAuditory feature extractionAuditoryfilterbankCochlear modelFeature extractionGammachirp filterbank

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Speech Science

Background:

  • Binary voice classification offers limited clinical utility for distinguishing specific pathology types.
  • Accurate multi-class classification is essential for effective pathological voice recognition (PVR).
  • Existing feature extraction methods often fall short in capturing the nuances of pathological voices.

Purpose of the Study:

  • To introduce a novel Auditory Feature Extraction (AFE) approach for robust multi-class PVR.
  • To simulate human auditory perception using a Gammachirp FilterBank (GCFB) for enhanced voice feature extraction.
  • To evaluate the AFE approach's performance against state-of-the-art methods on benchmark datasets.

Main Methods:

  • Developed an AFE approach employing a Gammachirp FilterBank (GCFB) with 128 filters, simulating cochlear spectral behavior.
  • Applied decimation, cubic-root amplitude compression, and Discrete Cosine Transform to GCFB outputs to generate AFE coefficients.
  • Utilized the Hidden Markov Model Toolkit for evaluating AFE performance on the Saarbruecken Voice Database (SVD) and MEEI dataset.

Main Results:

  • The AFE approach achieved 99.75% balanced accuracy in binary classification and 94.38% in multi-class classification on the SVD dataset.
  • AFE significantly outperformed HFCC (95.6%), FDLP (94.8%), and MFCC (93.85%) in binary classification on SVD.
  • AFE demonstrated superior multi-class classification accuracy (94.38%) compared to HFCC (72.93%), FDLP (69.66%), and MFCC (60.03%) on SVD.
  • Achieved 100% balanced accuracy on the MEEI database for pathological voice classification.

Conclusions:

  • The proposed AFE approach provides a highly discriminative feature set for voice pathology classification.
  • AFE's performance suggests potential for improved clinical screening and diagnosis of voice disorders.
  • The Gammachirp FilterBank-based feature extraction offers a promising direction for advanced PVR systems.