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A Weakly Supervised Learning Framework for Detecting Social Anxiety and Depression.

Asif Salekin1, Jeremy W Eberle2, Jeffrey J Glenn2

  • 1Department of Computer Science, University of Virginia, Charlottesville, VA, 22903, USA.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
|June 13, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel weakly supervised learning framework to detect social anxiety and depression from speech. The method significantly improves the accuracy of identifying individuals with these mental health conditions.

Keywords:
Social anxietyanxietyassessmentaudio worddepressionembeddingfeature modelingmental disordermultiple instance learningspeechweakly labeledweakly supervised learning

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Area of Science:

  • Computational linguistics
  • Psychiatry
  • Machine learning

Background:

  • Social anxiety and depression are prevalent but often underdiagnosed due to reliance on subjective assessments.
  • Current diagnostic methods are limited by biases and difficulties in identifying individuals needing support.
  • Objective markers, like speech features, could enhance early detection and intervention for mental health conditions.

Purpose of the Study:

  • To develop a weakly supervised learning framework for detecting social anxiety and depression from audio data.
  • To introduce NN2Vec, a novel feature modeling technique, to capture the relationship between vocal states and mental health symptoms.
  • To present BLSTM-MIL, a multiple instance learning adaptation of a BLSTM classifier, for improved detection accuracy.

Main Methods:

  • Proposed a weakly supervised learning framework for analyzing long audio clips.
  • Introduced NN2Vec for modeling the relationship between vocal states and affective states.
  • Utilized a BLSTM-MIL classifier with NN2Vec features for detecting social anxiety and depression.

Main Results:

  • NN2Vec features improved detection of social anxiety and depression symptoms by 17% and 13% respectively, compared to baselines.
  • The combined framework achieved F-1 scores of 90.1% for social anxiety and 85.44% for depression.
  • Demonstrated the efficacy of weakly supervised learning in identifying mental health conditions from speech.

Conclusions:

  • Weakly supervised learning offers a practical approach for detecting social anxiety and depression from speech data.
  • The NN2Vec and BLSTM-MIL framework shows significant potential for objective, scalable mental health assessment.
  • This approach could aid in early identification and treatment of individuals with social anxiety and depression.