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Data Collection for Automatic Depression Identification in Spanish Speakers Using Deep Learning Algorithms: Protocol

Luis F Brenes1, Luis A Trejo1, Jose Antonio Cantoral-Ceballos1

  • 1School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico.

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|July 31, 2025
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Summary
This summary is machine-generated.

This study introduces a new dataset for voice depression classification in Spanish speakers, utilizing both professional and smartphone recordings. This advances objective mental health diagnosis through deep learning models.

Keywords:
automated depression diagnosisdeep learning for audio analysisdepression Spanish language datasetdepression binary classificationhigh-quality and smartphone audio recordingshigh-quality audio data

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Area of Science:

  • Computational linguistics
  • Mental health informatics
  • Machine learning for healthcare

Background:

  • Depression diagnosis relies on subjective questionnaires, lacking objective biomarkers.
  • Deep learning models for voice depression classification require extensive, multilingual datasets, which are currently scarce.
  • Existing voice analysis for depression is limited by data availability and language diversity.

Purpose of the Study:

  • To establish a high-quality voice dataset for depression classification in Spanish speakers.
  • To facilitate deep learning research by providing a novel dataset.
  • To explore the utility of smartphone recordings for depression detection.

Main Methods:

  • Collected voice recordings from at least 60 Spanish-speaking participants diagnosed with depression and controls.
  • Utilized both professional-grade and smartphone microphones for data capture.
  • Gathered depression labels via the Patient Health Questionnaire-9 and other relevant speech-influencing data.

Main Results:

  • The created dataset enables immediate research into Spanish-language voice depression classification.
  • Facilitates evaluation of audio quality's impact on deep learning models.
  • Supports research on the practical application of voice depression classification via smartphone apps.

Conclusions:

  • This work contributes to objective, automated depression classification using voice analysis.
  • Addresses data scarcity and language barriers in deep learning for mental health.
  • Paves the way for advanced, accessible AI-driven mental health diagnostic tools.