## 4 Results  
### Experiments  
The model was trained multiple times to compare the values of single questions, multiple questions, and multiple modalities. Each modality was trained once on a single question, and included in a combined model with all modalities. The language-only model trained on all questions using a multi-response block was compared with a combined model. Scores were measured on five folds, with care taken to ensure the same proportion of severity levels in each fold. The model was validated using two separate metrics, i.e., the area under the receiver operating characteristics curve (AUROC) for predicting a binary classification (depressed or not depressed), and the Pearson's correlation coefficient (R Score) for detecting exact survey scores. All AUROC scores were binary measures where any score 10 or greater was labeled as a positive identification of depression or anxiety symptomatology. Results are shown in Table 1, and show that language provided substantial predictive value. This aligns with expectations, since the language modality can most easily take advantage of the responses to the questions adapted from the PHQ-2 and GAD-2. Predictive value was also found in audio and facial expressions, with audio results less predictive than text, and facial expressions less than audio. It is hypothesized that some fraction of the value in text comes from the chosen questions, and future work is needed to determine how well text would perform when answering more general questions. It is evident that moving from a single question to multiple questions also improved the accuracy of the model. This was expected, although the amount by which things were improved was less than anticipated. Nonetheless, the result implies that with even a single, well-worded open-ended question, reasonable predictions can be made. Combining multiple modalities did not improve the performance of the model. Even though each modality itself showed predictive value, the combination was no better than \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **Modality** & **AUC (PHQ)** & **AUC (GAD)** & **R (PHQ)** & **R (GAD)** \\ \hline \multirow{4}{*}{Single question} & Language & 0.856 & 0.863 & 0.702 & 0.708 \\  & Audio & 0.774 & 0.763 & 0.524 & 0.515 \\  & Facial features & 0.680 & 0.710 & 0.378 & 0.410 \\  & All combined & 0.848 & 0.871 & 0.713 & 0.703 \\ \hline \multirow{2}{*}{All five questions} & Language & **0.903** & **0.909** & 0.806 & 0.799 \\  & All combined & 0.901 & 0.906 & **0.814** & **0.801** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results for each modality for a single question (top rows) as well as all questions (bottom rows). Models are scored on the area under the receiver operating characteristics curve (AUC) when the model was scored as a classifier (positive cases when PHQ-9/GAD-7 scored \(\geq 10\)), and Pearson correlation (R) when the model was scored as a regressor. Bold values show the best score in each column. the language-only version. This result is counterintuitive to expectations and is discussed further in the next section. The final PHQ-V and GAD-V models use language alone because it provided similar value to the combined, but resulted in models being simpler to use and deploy. With an AUC score above a 0.90 for both PHQ-V and GAD-V and correlations above 0.80, these models were shown to strongly predict the traditional PHQ-9 and GAD-7 assessments. They offer a good replacement in cases where an asynchronous video assessment is more convenient than traditional multiple choice assessments. The Receiver Operating Characteristic (ROC) curve of the language only model is shown in Figure 5, demonstrating its strong performance as a classifier. And the distribution of scores plotted against their predictions are shown in Figure 6, showing its strong performance as a regressor. Figure 5: Receiver operating characteristics curve for (**a**) PHQ-V predictions and (**b**) GAD-V predictions.