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2018 | OriginalPaper | Chapter

29. Automatic Detection of Depressive States from Speech

Authors : Aditi Mendiratta, Filomena Scibelli, Antonietta M. Esposito, Vincenzo Capuano, Laurence Likforman-Sulem, Mauro N. Maldonato, Alessandro Vinciarelli, Anna Esposito

Published in: Multidisciplinary Approaches to Neural Computing

Publisher: Springer International Publishing

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Abstract

This paper investigates the acoustical and perceptual speech features that differentiate a depressed individual from a healthy one. The speech data gathered was a collection from both healthy and depressed subjects in the Italian language, each comprising of a read and spontaneous narrative. The pre-processing of this dataset was done using Mel Frequency Cepstral Coefficient (MFCC). The speech samples were further processed using Principal Component Analysis (PCA) for correlation and dimensionality reduction. It was found that both groups differed with respect to the extracted speech features. To distinguish the depressed group from the healthy one on the basis the proposed speech processing algorithm the Self Organizing Map (SOM) algorithm was used. The clustering accuracy given by SOM’s was 80.67%.

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Title: Automatic Detection of Depressive States from Speech
Authors: Aditi Mendiratta
Filomena Scibelli
Antonietta M. Esposito
Vincenzo Capuano
Laurence Likforman-Sulem
Mauro N. Maldonato
Alessandro Vinciarelli
Anna Esposito
Publisher: Springer International Publishing
Book: Multidisciplinary Approaches to Neural Computing
Print ISBN: 978-3-319-56903-1

Electronic ISBN: 978-3-319-56904-8

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-56904-8_29

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