![]() ![]() Data collection settings and data cleaning are crucial when considering automated voice analysis for clinical purposes. ConclusionĪ generalizable speech emotion recognition model can effectively reveal changes in speaker depressive states before and after remission in patients with MDD. Background noise (but not speaker diarization) heavily impacted predictions. ![]() Model predictions were stable throughout the interview, suggesting that 20–30 s of speech might be enough to accurately screen a patient. Further, speech from patients in remission was indistinguishable from that of the control group. ![]() The model showed separation between healthy controls and depressed patients at the first visit, obtaining an AUC of 0.71. The model was evaluated on raw, de-noised, and speaker-diarized data. For the Danish twins, the same regression model was fitted but by. We examined the model's predictive ability to classify the presence of depression on Danish speaking healthy controls ( N = 42), patients with first-episode major depressive disorder (MDD) ( N = 40), and the subset of the same patients who entered remission ( N = 25) based on recorded clinical interviews. These data are not perfectly normally distributed in that the residuals about the zero. MethodsĪ Mixture of Experts machine learning model was trained to infer happy/sad emotional state using three publicly available emotional speech corpora in German and US English. We investigated a generalizable approach to aid clinical evaluation of depression and remission from voice using transfer learning: We train machine learning models on easily accessible non-clinical datasets and test them on novel clinical data in a different language. Affective disorders are associated with atypical voice patterns however, automated voice analyses suffer from small sample sizes and untested generalizability on external data. ![]()
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January 2023
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