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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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A software pipeline for systematizing machine learning of speech data.

Jimuel Celeste1, Mashrura Tasnim1, Amable J Valdés Cuervo2

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

Frontiers in Psychiatry
|August 13, 2025
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Summary
This summary is machine-generated.

Researchers developed configurable Python pipelines to enhance the reproducibility of machine learning experiments in speech analysis for mental health and neurocognitive conditions. Sharing data and configurations ensures reliable results in speech-based diagnostics.

Keywords:
aphasiadementiadepressiondigital mental healthmachine learning for speech audiosoftware pipeline for speech signal processingspeech analysis

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Area of Science:

  • Computational linguistics
  • Machine learning
  • Speech analysis

Background:

  • Reproducibility is crucial in science, especially in machine learning.
  • Sharing datasets is common, but sharing configurations is needed for full replication.
  • Speech analysis for mental health conditions requires reproducible methods.

Purpose of the Study:

  • To develop a software pipeline for reproducible speech data preprocessing and machine learning.
  • To facilitate the sharing of data, preprocessing, and model configurations.
  • To enable replication of machine learning studies in speech analysis.

Main Methods:

  • Developed a suite of configurable Python Luigi pipelines.
  • Included components for speech data preprocessing, feature extraction, cross-validation, and model training.
  • Utilized state-of-the-art libraries like scikit-learn, openSMILE, and LogMMSE.

Main Results:

  • Successfully replicated three distinct machine learning studies.
  • Demonstrated the platform's capability in detecting depression, mild cognitive impairment, and aphasia from speech.
  • Validated the effectiveness of the configurable pipelines for reproducibility.

Conclusions:

  • The developed software pipelines significantly improve the reproducibility of speech analysis experiments.
  • Sharing configurations alongside data is essential for robust scientific findings.
  • This platform supports reliable research in clinical speech analysis.