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Related Experiment Video

Updated: Dec 22, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Voice Signal Typing Using a Pattern Recognition Approach.

J M Miramont1, Juan F Restrepo1, J Codino2

  • 1Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática, UNER-CONICET, Oro Verde, Entre Ríos, Argentina.

Journal of Voice : Official Journal of the Voice Foundation
|May 8, 2020
PubMed
Summary

This study introduces an automated system for voice signal typing, crucial for analyzing voice disorders. The pattern recognition approach achieved over 82% accuracy, offering a reliable, objective method for clinicians.

Keywords:
Pattern recognitionSupport vector machineVoice signal classificationVoice signal typing

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Area of Science:

  • Speech processing
  • Biomedical engineering
  • Pattern recognition

Background:

  • Voice signal typing is essential for perturbation analysis but is time-consuming and subjective.
  • Objective, automated methods are needed to improve the efficiency and reliability of voice signal classification.

Purpose of the Study:

  • To develop and validate a pattern recognition system for automatic voice signal typing.
  • To assess the generalizability of the proposed system across different datasets.

Main Methods:

  • A multi-class linear Support Vector Machine (SVM) was employed.
  • Features included Jitter, Shimmer, Harmonic-to-Noise Ratio, Cepstral Prominence Peak, nonlinear dynamics measures, and two novel objective parameters.
  • Validation involved 1262 voice signals from two corpora using cross-dataset experiments and cross-validation.

Main Results:

  • Statistically significant differences were found for all features across the three voice types.
  • The system achieved accuracies exceeding 82.71% in both intra-dataset and inter-dataset evaluations.
  • Posterior probabilities were proposed as a reliability measure for the assigned voice type.

Conclusions:

  • The proposed pattern recognition approach effectively discriminates among three voice types.
  • This automated tool has the potential to assist clinicians in making more informed decisions regarding voice signal classification.
  • The system demonstrates generalizability, paving the way for future fully automatic voice analysis tools.