## 5 Conclusions  
In this article, we presented and analysed a processing pipeline that is capable of automatically detecting practitioner competence by evaluating the recordings of guided self-help sessions for patients with mild-to-moderate anxiety. The pipeline mentioned above consisted of two main sub-modules: ASR and the automatic detection of the practitioner's competence in recordings of real guided self-help sessions. The ASR system achieved relatively low performance, which is a reflection of the challenging data and not out of line with similar systems on other health domain data. Figure 8: The confusion matrix results of the practitioner’s levels of competence (low, medium, high) based on the patient’s level of depression (normal, mild, moderate, moderately severe, and severe). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{3}{c}{**Automatic Transcripts**} & \multicolumn{3}{c}{**Manual Transcripts**} \\ \cline{2-7}  & **Precision** & **Recall** & **Accuracy** & **Precision** & **Recall** & **Accuracy** \\ \hline SVM & 0.61 (0.11) & 0.53 (0.21) & 56.86\% & **0.92 (0.03)** & **0.91 (0.02)** & **90.20\%** \\ Decision Tree & 0.54 (0.09) & 0.51 (0.13) & 53.92\% & 0.63 (0.08) & 0.65 (0.07) & 66.66\% \\ Random Forest & 0.51 (0.08) & 0.49 (0.16) & 52.94\% & 0.62 (0.07) & 0.59 (0.11) & 62.09\% \\ Ada-Boost & 0.53 (0.07) & 0.49 (0.16) & 52.94\% & 0.57 (0.06) & 0.55 (0.12) & 58.33\% \\ Gradient Boosting & 0.52 (0.07) & 0.50 (0.17) & 53.33\% & 0.61 (0.06) & 0.57 (0.10) & 59.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The level of competence classification results using several classifiers for manual and automatic transcriptions (the standard deviation of the precision and recall). Figure 7: The confusion matrix results of the practitioner’s level of competence (low, medium, high) based on the patient’s level of anxiety (normal, mild, moderate and severe). Despite that, the automatic transcriptions achieved from the ASR system are considered a major part of this study, and the corresponding prediction results are comparable to those achieved with the manual transcriptions with a good level of accuracy. As for the sub-module related to the automatic detection of the practitioner's competence, we focused on predicting and classifying the treatment competency measures as discrete scores and as levels of competence. Both the accuracy (90.20%) and the correlation coefficients (0.92) results denote higher classification and prediction results on the manual transcriptions, which declare the ability of the machine learning models to detect the practitioner's competence in real settings with the use of both acoustic and language-based features. Furthermore, the prediction of the treatment competency measures is deployed based on each item on the rating scale. The results showed that the items that depend on more personal qualifications related to the practitioners gained higher prediction results than the other items. That is a confirmed sign of the practitioner's language showing signs of competence in the session. In addition, the results are presented based on the features that gained higher performance results. Those features indicated signs of a ruptured therapeutic alliance based on what was reported in [26]. To conclude, the deployment of the system developed here in real-world busy clinical settings and the context of a pragmatic randomised clinical trial could enhance the therapeutic experience for the patient and guarantee fast and low-cost feedback for clinical supervisors. Such feedback would be invaluable both in training qualified practitioners and effectively supervising qualified practitioners, improving the delivery of interventions, and potentially positively impacting the patients' experience and outcomes. In future work, the real deployment of such a tool will give insights into its acceptability in routine clinical services.