| Abstract
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<p>Detection of mild laryngeal disor … <p>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.</p>ical cases. © 2014 Elsevier B.V.</p>
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| Author
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Evaldas Vaiciukynas +
, Antanas Verikas +
, Adas Gelzinis +
, Marija Bacauskiene +
, Zvi Kons +
, Aharon Satt +
, Ron Hoory +
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| DOI
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http://dx.doi.org/10.1016/j.asoc.2014.01.012 +
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| Diva
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http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:698528
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| EndPage
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103 +
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| Issue
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May 2014 +
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| Journal
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Applied Soft Computing +
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| PublicationType
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Journal Paper +
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| Publisher
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Elsevier +
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| StartPage
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91 +
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| Title
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Fusion of voice signal information for detection of mild laryngeal pathology +
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| Volume
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18 +
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| Year
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2014 +
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| Has queryThis property is a special property in this wiki.
|
Publications:Fusion of voice signal information for detection of mild laryngeal pathology +
, Publications:Fusion of voice signal information for detection of mild laryngeal pathology +
, Publications:Fusion of voice signal information for detection of mild laryngeal pathology +
, Publications:Fusion of voice signal information for detection of mild laryngeal pathology +
, Publications:Fusion of voice signal information for detection of mild laryngeal pathology +
, Publications:Fusion of voice signal information for detection of mild laryngeal pathology +
, Publications:Fusion of voice signal information for detection of mild laryngeal pathology +
, Publications:Fusion of voice signal information for detection of mild laryngeal pathology +
, Publications:Fusion of voice signal information for detection of mild laryngeal pathology +
, Publications:Fusion of voice signal information for detection of mild laryngeal pathology +
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| Categories |
Publication +
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| Modification dateThis property is a special property in this wiki.
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30 September 2016 20:38:59 +
|
