Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/12393
Authors: Mendiratta, Aditi* 
Scibelli, Filomena* 
Esposito, Antonietta M.* 
Capuano, Vincenzo* 
Likforman-Sulem, Laurence* 
Maldonato, Mauro N.* 
Vinciarelli, Alessandro* 
Esposito, Anna* 
Title: Automatic Detection of Depressive States from Speech
Issue Date: 2018
URI: http://hdl.handle.net/2122/12393
ISBN: 978-3319569031
Keywords: Self Organizing Maps (SOM)
PCA
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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