Depressive and Mania Mood State Detection through Voice as a Biomarker Using Machine Learning
Bibliographic record
Abstract
Abstract Background Both depressive and mania mood state have high prevalence and are important causes of social burden worldwide, however, there is still no objective indicator for detection. This study aimed to examine if voice could be used as a biomarker to detect these symptoms in China. Methods 1,287 voice messages from 81 subjects were classified into three groups: the depression mood state group (406 voice messages from n = 31), the mania mood state group (192 voice messages from n = 14), and the remission group (689 voice messages from n = 36), based on the scores of the MDQ, QIDS and YMRS. 34 features were extracted from voice records which is collected in real-world emotional diary. A three-group comparison was performed through analysis of Kruskal-Wallis H Test. Three feature extraction methods were adopted and four machine learning methods were performed. Results 33 voice indicators showed differences among the three groups(p < 0.05). Among the machine learning methods, the best performance was obtained using the Gate Recurrent Unit with 79.6% sensitivity, 91.1% specificity and 82.5% sensitivity, 90.7% specificity for the detection of depressive and mania mood state respectively. Conclusions This study further revealed participants with depressive or manic mood state could be accurately distinguished through machine learning. Although this study is limited by a small sample size, it is the first study on voice as a biomarker in both depressive and mania mood state which suggests the possibility of detecting these mood states through voice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".