Spectral Entropy in Speech for Classification of Depressed Speakers

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Publication Details

Author listYingthawornsuk T.

PublisherHindawi

Publication year2017

Start page679

End page682

Number of pages4

ISBN9781509056989

ISSN0146-9428

eISSN1745-4557

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85019186167&doi=10.1109%2fSITIS.2016.113&partnerID=40&md5=df678be8bd767794d7cc5f825a6a4eac

LanguagesEnglish-Great Britain (EN-GB)


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Abstract

This paper presents a study of spectral entropy analysis on speech for the possible prediction of depression in speakers who are at risk of committing suicide, when the symptom of depression strikes, unless admitted and having a proper treatment in time. Prediction is primarily necessary task to prevention of that life-threatening risk. In this study the full-band and further sub-band entropies of eight evenly separated frequency bands of 625 Hz estimated from the female voiced segments were computationally extracted and consequently used to form the parameter models for between-group classifications. The average of correct classification is considered to be fairly high when training a ML classifier with the 35% of extracted sample database and testing it again with the rest of sample database. As result shown, the classifying percentage obtained from study has suggested the higher frequency sub-band entropies extracted from spoken sound capable of being group discrimination between two categorized speakers. ฉ 2016 IEEE.


Keywords

DepressionSpectral entropySpeech


Last updated on 2023-29-09 at 07:35