Analysis and Detection of Pathological Voice using Glottal Source Features

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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 an 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.
Original languageEnglish
Article number8926347
Pages (from-to)367-379
JournalIEEE Journal of Selected Topics in Signal Processing
Volume14
Issue number2
Early online date2019
DOIs
Publication statusPublished - Feb 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Speech analysis
  • Pathological voice
  • Pathology detection
  • Entropy
  • Cepstral analysis

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  • Projects

    Interdisciplinary research on statistical parametric speech synthesis

    Juvela, L., Airaksinen, M., Bollepalli, B., Bäckström, T., Pohjalainen, J., Kakouros, S., Gowda, D., Jokinen, E. & Alku, P.

    01/01/201531/12/2017

    Project: Academy of Finland: Other research funding

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