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Detecting Depression from Voice

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Advances in Artificial Intelligence (Canadian AI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11489))

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Abstract

In this paper, we present our exploration of different machine-learning algorithms for detecting depression by analyzing the acoustic features of a person’s voice. We have conducted our study on benchmark datasets, in order to identify the best framework for the task, in anticipation of deploying it in a future application.

This work has been partially funded by the GRA Rice Graduate Scholarship in Communications, the AGE-WELL NCE and NSERC.

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Author information

Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, AB, Canada

    Mashrura Tasnim & Eleni Stroulia

Authors
  1. Mashrura Tasnim
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  2. Eleni Stroulia
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Corresponding author

Correspondence to Mashrura Tasnim .

Editor information

Editors and Affiliations

  1. University of Quebec in Montreal, Montreal, QC, Canada

    Marie-Jean Meurs

  2. University of Toronto, Toronto, ON, Canada

    Frank Rudzicz

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Tasnim, M., Stroulia, E. (2019). Detecting Depression from Voice. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_47

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  • DOI: https://doi.org/10.1007/978-3-030-18305-9_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18304-2

  • Online ISBN: 978-3-030-18305-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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