A software pipeline for systematizing machine learning of speech data

• Published in: Frontiers in psychiatry Vol. 16; p. 1451368
• Main Authors: Celeste, Jimuel, Tasnim, Mashrura, Valdés Cuervo, Amable J., de la Cal, Enrique A., Stroulia, Eleni
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 2025
• Subjects: aphasia; dementia; depression; digital mental health; machine learning for speech audio; Psychiatry; speech analysis; aphasia; software pipeline for speech signal processing; machine learning for speech audio; dementia; digital mental health; depression; speech analysis
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2025.1451368

The reproducibility and replicability of experimental findings is an essential element of the scientific process. The machine-learning community has a long-established practice of sharing data sets so that researchers can report the performance of their models on the same data. In the area of speech analysis, and more specifically speech of individuals with mental health and neurocognitive conditions, a number of such data sets exist and are the subject of organized “challenge tasks”. However, as the complexity of the available relevant software libraries and their parameters increases, we argue that researchers should not only share their data but also their preprocessing and machine learning configurations so that their experiments may be fully reproduced. This is why we have designed and developed a suite of configurable software pipelines with Python Luigi for speech-data preprocessing, feature extraction, fold construction for cross-validation, machine learning training, and label prediction. These components rely on state-of-the-art software libraries, frequently used by researchers, and implement many typical tasks in this field, i.e., scikit-learn, openSMILE, LogMMSE, so that, given the configuration parameters of each task, any underlying experiments can be readily reproduced. We have evaluated our platform by replicating three different machine learning studies, with the aim of detecting depression, mild cognitive impairment, and aphasia from speech data.