@inproceedings{martin-rouas-2024-voice,
    title = "Why Voice Biomarkers of Psychiatric Disorders Are Not Used in Clinical Practice? Deconstructing the Myth of the Need for Objective Diagnosis",
    author = "Martin, Vincent P.  and
      Rouas, Jean-Luc",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1531",
    pages = "17603--17613",
    abstract = "Given the high prevalence of mental disorders and the significant diagnostic delays and difficulties in patient follow-up, voice biomarkers hold the promise of improving access to care and therapeutic follow-up for people with psychiatric disorders. Yet, despite many years of successful research in the field, none of these voice biomarkers are implemented in clinical practice. Beyond the reductive explanation of the lack of explainability of the involved machine learning systems, we look for arguments in the epistemology and sociology of psychiatry. We show that the estimation of diagnoses, the major task in the literature, is of little interest to both clinicians and patients. After tackling the common misbeliefs about diagnosis in psychiatry in a didactic way, we propose a paradigm shift towards the estimation of clinical symptoms and signs, which not only address the limitations raised against diagnosis estimation but also enable the formulation of new machine learning tasks. We hope that this paradigm shift will empower the use of vocal biomarkers in clinical practice. It is however conditional on a change in database labeling practices, but also on a profound change in the speech processing community{'}s practices towards psychiatry.",
}

Given the high prevalence of mental disorders and the significant diagnostic delays and difficulties in patient follow-up, voice biomarkers hold the promise of improving access to care and therapeutic follow-up for people with psychiatric disorders. Yet, despite many years of successful research in the field, none of these voice biomarkers are implemented in clinical practice. Beyond the reductive explanation of the lack of explainability of the involved machine learning systems, we look for arguments in the epistemology and sociology of psychiatry. We show that the estimation of diagnoses, the major task in the literature, is of little interest to both clinicians and patients. After tackling the common misbeliefs about diagnosis in psychiatry in a didactic way, we propose a paradigm shift towards the estimation of clinical symptoms and signs, which not only address the limitations raised against diagnosis estimation but also enable the formulation of new machine learning tasks. We hope that this paradigm shift will empower the use of vocal biomarkers in clinical practice. It is however conditional on a change in database labeling practices, but also on a profound change in the speech processing community{'}s practices towards psychiatry.
Martin, Vincent P. Rouas, Jean-Luc
Why Voice Biomarkers of Psychiatric Disorders Are Not Used in Clinical Practice? Deconstructing the Myth of the Need for Objective Diagnosis