Analysis and Detection of Pathological Voice using Glottal Source Features

TL;DR

This paper systematically analyzes glottal source features derived from various methods for detecting voice pathologies, demonstrating their effectiveness and complementarity with traditional features like MFCCs and PLP.

Contribution

It introduces a comprehensive analysis of glottal source features, including novel MFCC derivation from glottal waveforms, and shows their improved performance in pathology detection.

Findings

Glottal source features contain discriminative information for voice pathology.

Combining glottal features with MFCCs and PLP improves detection accuracy.

Glottal features perform comparably or better than conventional spectral features.

Abstract

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 Principe de Asturias (HUPA) database and the Saarbrucken 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.