Analysis and Detection of Pathological Voice Using Glottal Source Features

Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features and investigates their effectiveness in voice pathology detection. Glottal source features are extracted using glottal...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing 2020-02, Vol.14 (2), p.367-379
Main Authors: Kadiri, Sudarsana Reddy, Alku, Paavo
Format: Article
Language:English
Subjects:
Acoustic measurements
Cepstral analysis
Computation
Entropy
Experiments
Feature extraction
Filtration
Glottal flow waveform
Glottal inverse filtering
Glottal source features
Linear prediction
Pathological voice
Pathology
Pathology detection
Perturbation methods
Speech analysis
Support vector machines
Voice recognition
Waveforms
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Summary:Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features and investigates their effectiveness in voice pathology detection. Glottal source features are extracted using glottal flows estimated with the quasi-closed phase (QCP) glottal inverse filtering method, using approximate glottal source signals computed with the zero frequency filtering (ZFF) method, and using acoustic voice signals directly. In addition, we propose to derive mel-frequency cepstral coefficients (MFCCs) from the glottal source waveforms computed by QCP and ZFF to effectively capture the variations in glottal source spectra of pathological voice. Experiments were carried out using two databases, the Hospital Universitario Príncipe de Asturias (HUPA) database and the Saarbrücken Voice Disorders (SVD) database. Analysis of features revealed that the glottal source contains information that discriminates normal and pathological voice. Pathology detection experiments were carried out using support vector machine (SVM). From the detection experiments it was observed that the performance achieved with the studied glottal source features is comparable or better than that of conventional MFCCs and perceptual linear prediction (PLP) features. The best detection performance was achieved when the glottal source features were combined with the conventional MFCCs and PLP features, which indicates the complementary nature of the features.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2019.2957988