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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Acknowledgements
The patients, healthy subjects (with typical speech) and doctors (psychiatrists) are acknowledged for their involvement and contribution towards this research. The International Institute for Advanced Scientific Studies (IIASS) and Professor Ferdinando Mancini (President, IIASS), is acknowledged for having supported the first author during her internship.
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Mendiratta, A. et al. (2018). Automatic Detection of Depressive States from Speech. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_29
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DOI: https://doi.org/10.1007/978-3-319-56904-8_29
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