## 1 Introduction
Concerns have been raised relating to manual competency rating processes, due to it being very time-consuming, the variability of ratings and the bias of ratings applied by supervisors who might know the practitioners [11, 12]. Therefore, there is a distinct need for an automatic tool to predict practitioners' competency ratings to improve the efficiency of the process, reduce bias and improve patient care. In additional, the practitioner's skills, such as competence, have been found to contribute positively to the therapeutic alliance. The practitioner's personal qualities and techniques have a positive influence on the understanding or repair of ruptures. That is, any deterioration in the therapeutic alliance [13, 14]. A system for the automatic prediction of the practitioner's competence scores could therefore assist in defining the existence of a positive or ruptured therapeutic alliance in the session. This paper investigates the automatic detection of psychotherapeutic competence. It explores both the problem of classifying the level of competency as well as that of predicting the competency measure as a total and item-based scores. The use of both acoustic and linguistic features is adopted in the study to explore the features that truly map to the treatment competency in the session. Experiments have been conducted using a dataset of real audio recordings. Both the patient's and practitioner's speech are used for the feature extraction stage in the machine learning pipeline. The features that achieved high performance in the system are explored based on the therapeutic alliance perspective in the study. The study therefore investigates the potential efficacy of implementing an automatic system for detecting the practitioner's competence in real clinical settings by the use of acoustic and linguistic features. The system will use both acoustic and linguistic features. Our earlier results reported in [15] demonstrated the feasibility of predicting the continuous emotional labels from the guided self-help sessions dataset used in this study using acoustic features with respect to practitioner' competency. The acoustic features that demonstrated efficient prediction results in the study were the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) and the Bag of Audio Words (BoAWs). Therefore, those acoustic features are adopted in this paper as a suitable indicator for predicting the practitioner's competency measure. The speakers' language in sessions could also reveal the emotions present during the session, along with the strength of those emotions. The use of language-based (linguistic) features to capture speakers' emotions could also improve the accuracy of the prediction of practitioner competency. The Referential Activity (RA) is a dictionary-based model that estimates the degree to which language is associated with emotions. It was built to analyse the language of therapeutic sessions and how it is connected to clinical evaluation. It depends on a set of functional stages that describe the general process of bringing non-verbal material that occurred both outside of and within awareness into a form that could be translated to language [16; 17]. Therefore, the RA model scores have been implemented as the language-based (linguistic) features in this study. The linguistic features could provide complementary information to the acoustic features. Consequently, the fusion of acoustic and linguistic features mentioned earlier is examined to predict the practitioner's competency. To the best of our knowledge, this is the first work to predict the competency of delivery of a low-intensity psychological intervention automatically using real patient-practitioner conversations drawn from treatment sessions. These interventions are being increasingly used in public health systems due to the low cost of training and delivery.