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A unified approach to suicide risk detection using text and voice.

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A23Q7VCMT" target="_blank" >RIV/00216208:11320/23:23Q7VCMT - isvavai.cz</a>

  • Result on the web

    <a href="https://researchbank.swinburne.edu.au/items/98ccb5a5-26b5-40ac-af12-c31cf6613647/1/Ravi_Iyer_Thesis.pdf" target="_blank" >https://researchbank.swinburne.edu.au/items/98ccb5a5-26b5-40ac-af12-c31cf6613647/1/Ravi_Iyer_Thesis.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    A unified approach to suicide risk detection using text and voice.

  • Original language description

    "The primary aim of this PhD thesis was to demonstrate the use of machine learningnmethods to predict suicide risk. The secondary aim was to argue that the accurate and timely identification of individuals in the community at high risk of suicide can best be achieved using a combination of voice signal analysis and transcribed speech-to-text analysis. The third and final aim was to argue that these research endeavours can only occur when underpinned by key ethical considerations."

  • Czech name

  • Czech description

Classification

  • Type

    O - Miscellaneous

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů