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Title page for ETD etd-12022013-124213


Type of Document Master's Thesis
Author Sinha, Abhraneel
Author's Email Address abhraneel.sinha@vanderbilt.edu
URN etd-12022013-124213
Title Human and machine recognition of the vocal characteristics of suicide
Degree Master of Science
Department Electrical Engineering
Advisory Committee
Advisor Name Title
Dr. D.Mitchell Wilkes Committee Chair
Dr. Ronald M.Saloman Committee Member
Keywords
  • Suicide
  • Speech
  • DSP
Date of Defense 2013-11-29
Availability unrestricted
Abstract
Suicide is a major health problem in the US, and has become an important topic of study. Recently, psychiatrists have reported hearing a particular sound or tonality in the voices of subjects that are at high risk of attempting suicide. This has lead to research to discriminate between depression and this high risk state based on the acoustic properties of the subject's voice, and has lead to some very promising results. Many of the features that have been used for this task are based on the power spectrum of the voice; however, it is not clear whether these features have captured the particular tonality that the psychiatrists have reported. The whole work attempts to address this question through analysis of speech data by calculating the harmonics, amplitudes, and other discriminating features from 6 depressed and 6 high risk subjects.
Files
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  SINHA.pdf 395.10 Kb 00:01:49 00:00:56 00:00:49 00:00:24 00:00:02

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