Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders

Abstract

A large proportion of Depression Disorder patients do not receive an effective diagnosis, which makes it necessary to find a more objective assessment to facilitate a more rapid and accurate diagnosis of depression. Speech data is easy to acquire clinically, its association with depression has been studied, although the actual predictive effect of voice features has not been examined. Thus, we do not have a general understanding of the extent to which voice features contribute to the identification of depression. In this study, we investigated the significance of the association between voice features and depression using binary logistic regression, and the actual classification effect of voice features on depression was re-examined through classification modeling. Nearly 1000 Chinese females participated in this study. Several different datasets was included as test set. We found that 4 voice features (PC1, PC6, PC17, PC24, P <0.05, corrected) made significant contribution to depression, and that the contribution effect of the voice features alone reached 35.65% ( Nagelkerke's R 2 ). In classification modeling, voice data based model has consistently higher predicting accuracy(F-measure) than the baseline model of demographic data when tested on different datasets, even across different emotion context. F-measure of voice features alone reached 81%, consistent with existing data. These results demonstrate that voice features are effective in predicting depression and indicate that more sophisticated models based on voice features can be built to help in clinical diagnosis.

Automated assessment of psychiatric disorders using speech: A systematic review

“…Other less studied variables that may affect speech and may vary within a group, thus inserting noise, are height, weight, dialectal variant, energetic state at the time of speech elicitation, and intimacy . If these variables are statistically different between case and control groups, they can be better matched through techniques such as propensity score matching …”

“…29 If these variables are statistically different between case and control groups, they can be better matched through techniques such as propensity score matching. 106…”

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

“…Speech has been demonstrated to have diagnostic validity for Alzheimer's disease (AD) and mild cognitive impairment (MCI) in studies using machine-learning classification models to differentiate individuals with AD/MCI from healthy individuals based on speech samples [34][35][36][37][38][39][40][41]. Additionally, speech analysis has been shown to be able to detect individuals with depression [42][43][44][45], schizophrenia [46][47][48][49], autism spectrum disorder [50], and Parkinson's disease [51,52], and can differentiate the subtypes of primary progressive aphasia and frontotemporal dementia [53][54][55]. Classification models provide diagnostic validity for speech measures and could be used to develop tools for disease screening and diagnosis.…”

Emerging coronavirus diseases and future perspectives

“…Study with enough evidence suggested that speech signals (such as certain vowel, words and numbers) contain vocal biomarkers that could be great aid to pathologist for the disease diagnosis. Several machine learning algorithms showed superior accuracy in disease prediction including Parkinson, depression, cardio and other diseases [38][39][40]. In the light of finding, it can be hypothesized that whether viral infections contain disease specific vocal biomarkers for disease analysis.…”