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Clinical depression analysis using speech features
, P. Agrawal, S.S. Dorle, D. Patil, V.M. Wadhai
Published in IEEE Computer Society
Pages: 111 - 112
Depression is a most common severe mental disturbance health disorder causing high societal costs. In clinical practice rating for depression depends almost on self questionnaires and clinical patient history report opinion. In recent years, the awareness has generated for automatic detection of depression from the speech signal. Some queries are raised that which features are more responsible for depression from speech and which classifiers gives good results. By identifying proper features from speech signal system even one can save the life of a patient. In this paper, a survey of speech signal features which relates for depression analysis is presented. Specially focused on adolescence speech. After surveying it is hypothesized that many speech features are there which are responsible for depression like linear features Prosodic, cepstral, spectral and glottal features and non-linear feature Teager energy operator (TEO). Some classification methods for depression analysis from previous studies are summarized. © 2013 IEEE.
About the journal
JournalData powered by TypesetInternational Conference on Emerging Trends in Engineering and Technology, ICETET
PublisherData powered by TypesetIEEE Computer Society