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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Wavelet adaptation for automatic voice disorders sorting.

Nafise Erfanian Saeedi¹, Farshad Almasganj

¹Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria 3010, Australia. n.erfaniansaeedi@student.unimelb.edu.au

Computers in Biology and Medicine

|May 15, 2013

Summary

This study introduces an adaptive wavelet feature extraction method for automatic voice disorder sorting. The approach successfully classifies six common vocal abnormalities, advancing early diagnosis of speech impairments.

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Area of Science:

Digital Speech Processing
Biomedical Signal Analysis
Machine Learning in Healthcare

Background:

Early diagnosis of voice disorders is crucial but challenging.
Existing methods struggle with comprehensive sorting of multiple vocal abnormalities.
Automatic classification of pathological voices requires advanced feature extraction.

Purpose of the Study:

To develop a comprehensive approach for automatic sorting of various vocal abnormalities.
To introduce an adaptive wavelet feature extraction method for voice signal analysis.
To improve the accuracy and scope of voice disorder classification.

Main Methods:

Utilized adaptive wavelets generated via a lattice structure for orthogonal wavelet parameterization.
Employed a genetic algorithm (GA) to optimize wavelet parameters based on classifier feedback.
Constructed a wavelet filterbank to decompose voice signals and extract eight energy-based features.
Applied a Support Vector Machine (SVM) for classifying voice signals using the extracted features.

Main Results:

The proposed method successfully sorted six types of vocal disorders: paralysis, nodules, polyps, edema, spasmodic dysphonia, and keratosis.
Adaptive wavelet features demonstrated high efficacy in discriminating between different pathological voice types.
Achieved full sorting accuracy for the tested vocal disorder categories.

Conclusions:

The adaptive wavelet feature extraction approach offers a powerful solution for automatic voice disorder sorting.
This method represents a significant advancement toward classifying a wider range of vocal system abnormalities.
The findings support the potential of digital speech processing for enhanced clinical diagnosis and management of voice disorders.