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Fusion of voice signal information for detection of mild laryngeal pathology
Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.ORCID iD: 0000-0003-2185-8973
Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
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2014 (English)In: Applied Soft Computing, ISSN 1568-4946, Vol. 18, no May 2014, 91-103 p.Article in journal (Refereed) Published
Abstract [en]

Detection of mild laryngeal disorders using acoustic parameters of human voice is the main objective in this study. Observations of sustained phonation (audio recordings of vocalized /a/) are labeled by clinical diagnosis and rated by severity (from 0 to 3). Research is exclusively constrained to healthy (severity 0) and mildly pathological (severity 1) cases - two the most difficult classes to distinguish between. Comprehensive voice signal characterization and information fusion constitute the approach adopted here. Characterization is obtained through diverse feature set, containing 26 feature subsets of varying size, extracted from the voice signal. Usefulness of feature-level and decision-level fusion is explored using support vector machine (SVM) and random forest (RF) as basic classifiers. For both types of fusion we also investigate the influence of feature selection on model accuracy. To improve the decision-level fusion we introduce a simple unsupervised technique for ensemble design, which is based on partitioning the feature set by k-means clustering, where the parameter k controls the size and diversity of the prospective ensemble. All types of the fusion resulted in an evident improvement over the best individual feature subset. However, none of the types, including fusion setups comprising feature selection, proved to be significantly superior over the rest. The proposed ensemble design by feature set decomposition discernibly enhanced decision-level and significantly outperformed feature-level fusion. Ensemble of RF classifiers, induced from a cluster-based partitioning of the feature set, achieved equal error rate of 13.1 ± 1.8% in the detection of mildly pathological larynx. This is a very encouraging result, considering that detection of mild laryngeal disorder is a more challenging task than a common discrimination between healthy and a wide spectrum of pathological cases. © 2014 Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2014. Vol. 18, no May 2014, 91-103 p.
Keyword [en]
Random forest, SVM, Angle modulated differential evolution, Feature selection, Ensemble of classifiers, Pathological voice
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-24665DOI: 10.1016/j.asoc.2014.01.012ISI: 000333056400008Scopus ID: 2-s2.0-84893974096OAI: oai:DiVA.org:hh-24665DiVA: diva2:698528
Available from: 2014-02-23 Created: 2014-02-23 Last updated: 2015-01-12Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
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