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  • 1.
    Uloza, Virgilijus
    et al.
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Padervinskis, Evaldas
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Uloziene, Ingrida
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Saferis, Viktoras
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.
    Combined Use of Standard and Throat Microphones for Measurement of Acoustic Voice Parameters and Voice Categorization2015Inngår i: Journal of Voice, ISSN 0892-1997, E-ISSN 1873-4588, Vol. 29, nr 5, s. 552-559Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Summary: Objective. The aim of the present study was to evaluate the reliability of the measurements of acoustic voice parameters obtained simultaneously using oral and contact (throat) microphones and to investigate utility of combined use of these microphones for voice categorization.

    Materials and Methods. Voice samples of sustained vowel /a/ obtained from 157 subjects (105 healthy and 52 pathological voices) were recorded in a soundproof booth simultaneously through two microphones: oral AKG Perception 220 microphone (AKG Acoustics, Vienna, Austria) and contact (throat) Triumph PC microphone (Clearer Communications, Inc, Burnaby, Canada) placed on the lamina of thyroid cartilage. Acoustic voice signal data were measured for fundamental frequency, percent of jitter and shimmer, normalized noise energy, signal-to-noise ratio, and harmonic-to-noise ratio using Dr. Speech software (Tiger Electronics, Seattle, WA).

    Results. The correlations of acoustic voice parameters in vocal performance were statistically significant and strong (r = 0.71–1.0) for the entire functional measurements obtained for the two microphones. When classifying into healthy-pathological voice classes, the oral-shimmer revealed the correct classification rate (CCR) of 75.2% and the throat-jitter revealed CCR of 70.7%. However, combination of both throat and oral microphones allowed identifying a set of three voice parameters: throat-signal-to-noise ratio, oral-shimmer, and oral-normalized noise energy, which provided the CCR of 80.3%.

    Conclusions. The measurements of acoustic voice parameters using a combination of oral and throat microphones showed to be reliable in clinical settings and demonstrated high CCRs when distinguishing the healthy and pathological voice patient groups. Our study validates the suitability of the throat microphone signal for the task of automatic voice analysis for the purpose of voice screening. Copyright © 2014 The Voice Foundation.

  • 2.
    Uloza, Virgilijus
    et al.
    Department of Otolaryngology, Kaunas University of Medicine, Eiveniu 2, LT-50009 Kaunas, Lithuania.
    Verikas, Antanas
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Bacauskiene, Marija
    Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
    Pribuisiene, Ruta
    Department of Otolaryngology, Kaunas University of Medicine, Eiveniu 2, LT-50009 Kaunas, Lithuania.
    Kaseta, Marius
    Department of Otolaryngology, Kaunas University of Medicine, Eiveniu 2, LT-50009 Kaunas, Lithuania.
    Saferis, Viktoras
    Department of Physics, Mathematics and Biophysics, Kaunas University of Medicine, Kaunas, Lithuania.
    Categorizing Normal and Pathological Voices: Automated and Perceptual Categorization2011Inngår i: Journal of Voice, ISSN 0892-1997, E-ISSN 1873-4588, Vol. 25, nr 6, s. 700-708Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objectives: The aims of the present study were to evaluate the accuracy of an elaborated automated voice categorization system that classified voice signal samples into healthy and pathological classes and to compare it with classification accuracy that was attained by human experts. Material and Methods: We investigated the effectiveness of 10 different feature sets in the classification of voice recordings of the sustained phonation of the vowel sound /a/ into the healthy and two pathological voice classes, and proposed a new approach to building a sequential committee of support vector machines (SVMs) for the classification. By applying “genetic search” (a search technique used to find solutions to optimization problems), we determined the optimal values of hyper-parameters of the committee and the feature sets that provided the best performance. Four experienced clinical voice specialists who evaluated the same voice recordings served as experts. The “gold standard” for classification was clinically and histologically proven diagnosis. Results: A considerable improvement in the classification accuracy was obtained from the committee when compared with the single feature type-based classifiers. In the experimental investigations that were performed using 444 voice recordings coming from 148 subjects, three recordings from each subject, we obtained the correct classification rate (CCR) of over 92% when classifying into the healthy-pathological voice classes, and over 90% when classifying into three classes (healthy voice and two nodular or diffuse lesion voice classes). The CCR obtained from human experts was about 74% and 60%, respectively. Conclusion: When operating under the same experimental conditions, the automated voice discrimination technique based on sequential committee of SVM was considerably more effective than the human experts.

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