دورية أكاديمية
Detecting subtle signs of depression with automated speech analysis in a non-clinical sample
| العنوان: | Detecting subtle signs of depression with automated speech analysis in a non-clinical sample |
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| المؤلفون: | König, Alexandra, Tröger, Johannes, Mallick, Elisa, Mina, Mario, Linz, Nicklas, Wagnon, Carole, Karbach, Julia, Kuhn, Caroline, Peter, Jessica |
| المساهمون: | Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
| المصدر: | ISSN: 1471-244X ; BMC Psychiatry ; https://hal.science/hal-03968260Test ; BMC Psychiatry, 2022, 22, ⟨10.1186/s12888-022-04475-0⟩. |
| بيانات النشر: | HAL CCSD BioMed Central |
| سنة النشر: | 2022 |
| المجموعة: | HAL Université Côte d'Azur |
| مصطلحات موضوعية: | Depressive symptoms Automated speech analysis Acoustic features Textual features Machine learning, Depressive symptoms, Automated speech analysis, Acoustic features, Textual features, Machine learning, [SCCO]Cognitive science |
| الوصف: | International audience ; Background: Automated speech analysis has gained increasing attention to help diagnosing depression. Most previous studies, however, focused on comparing speech in patients with major depressive disorder to that in healthy volunteers. An alternative may be to associate speech with depressive symptoms in a non-clinical sample as this may help to find early and sensitive markers in those at risk of depression. Methods: We included n = 118 healthy young adults (mean age: 23.5 ± 3.7 years; 77% women) and asked them to talk about a positive and a negative event in their life. Then, we assessed the level of depressive symptoms with a self-report questionnaire, with scores ranging from 0-60. We transcribed speech data and extracted acoustic as well as linguistic features. Then, we tested whether individuals below or above the cutoff of clinically relevant depressive symptoms differed in speech features. Next, we predicted whether someone would be below or above that cutoff as well as the individual scores on the depression questionnaire. Since depression is associated with cognitive slowing or attentional deficits, we finally correlated depression scores with performance in the Trail Making Test. Results: In our sample, n = 93 individuals scored below and n = 25 scored above cutoff for clinically relevant depressive symptoms. Most speech features did not differ significantly between both groups, but individuals above cutoff spoke more than those below that cutoff in the positive and the negative story. In addition, higher depression scores in that group were associated with slower completion time of the Trail Making Test. We were able to predict with 93% accuracy who would be below or above cutoff. In addition, we were able to predict the individual depression scores with low mean absolute error (3.90), with best performance achieved by a support vector machine. Conclusions: Our results indicate that even in a sample without a clinical diagnosis of depression, changes in speech relate to higher ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | hal-03968260; https://hal.science/hal-03968260Test; https://hal.science/hal-03968260/documentTest; https://hal.science/hal-03968260/file/Koenig2022-BMC.pdfTest |
| DOI: | 10.1186/s12888-022-04475-0 |
| الإتاحة: | https://doi.org/10.1186/s12888-022-04475-0Test https://hal.science/hal-03968260Test https://hal.science/hal-03968260/documentTest https://hal.science/hal-03968260/file/Koenig2022-BMC.pdfTest |
| حقوق: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.14D872F5 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1186/s12888-022-04475-0 |
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