Early prediction of major depression in adolescents using glottal wave characteristics and Teager Energy parameters

Autor:	Kuan Ee Brian Ooi, Nicholas B. Allen, Margaret Lech, Lu-Shih Alex Low
Rok vydání:	2012
Předmět:	Speech Acoustics Binary classification Speech recognition Early prediction Feature extraction otorhinolaryngologic diseases Feature (machine learning) Speech processing Psychology Energy (signal processing) Depression (differential diagnoses)
Zdroj:	ICASSP
DOI:	10.1109/icassp.2012.6288946
Popis:	Previous studies of an automated detection of Major Depression in adolescents based on acoustic speech analysis identified the glottal and the Teager Energy features as the strongest correlates of depression. This study investigates the effectiveness of these features in an early prediction of Major Depression in adolescents using a fully automated speech analysis and classification system. The prediction was achieved through a binary classification of speech recordings from 15 adolescents who developed Major Depression within two years after these recordings were made and 15 adolescents who did not developed Major Depression within the same time period. The results provided a proof of concept that an acoustic speech analysis can be used in early prediction of depression. The glottal features made the strongest predictors of depression with 69% accuracy, 62% specificity and 76% sensitivity. The TEO feature derived from glottal wave also provided good results, specifically when calculated at the frequency range of 1.3 kHz to 5.5 kHz.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6caf78dbca4c37b17a7c6e71024162e1 https://doi.org/10.1109/icassp.2012.6288946 Zobrazit plný text záznamu