Publications:Data dependent random forest applied to screening for laryngeal disorders through analysis of sustained phonation : Acoustic versus contact microphone

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Title Data dependent random forest applied to screening for laryngeal disorders through analysis of sustained phonation : Acoustic versus contact microphone
Author Antanas Verikas and Adas Gelzinis and Evaldas Vaiciukynas and Marija Bacauskiene and Jonas Minelga and Magnus Clarin and Virgilijus Uloza and Evaldas Padervinskis
Year 2015
PublicationType Journal Paper
Journal Medical Engineering and Physics
HostPublication
Conference
DOI http://dx.doi.org/10.1016/j.medengphy.2014.12.005
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:803439
Abstract Comprehensive evaluation of results obtained using acoustic and contact microphones in screening for laryngeal disorders through analysis of sustained phonation is the main objective of this study. Aiming to obtain a versatile characterization of voice samples recorded using microphones of both types, 14 different sets of features are extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We propose a new, data dependent random forests-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest is also presented. Experimental investigations using a mixed gender database of 273 subjects have shown that the perceptual linear predictive cepstral coefficients (PLPCC) was the best feature set for both microphones. However, the linear predictive coefficients (LPC) and linear predictive cosine transform coefficients (LPCTC) exhibited good performance in the acoustic microphone case only. Models designed using the acoustic microphone data significantly outperformed the ones built using data recorded by the contact microphone. The contact microphone did not bring any additional information useful for the classification. The proposed data dependent random forest significantly outperformed the traditional random forest. (C) 2015 IPEM. Published by Elsevier Ltd. All rights reserved.