## 1 Introduction
Furthermore, this is the first study that investigated the RA measures as language-based features extracted from the practitioner's and patient's speech to predict the practitioner's competency ratings. The study investigates the potential efficacy of implementing an automatic system for detecting the practitioner's competence in real clinical settings using acoustic and linguistic features. A growing body of literature recognises the importance of acoustic features in the field of therapy. A study examined the significance of several acoustic features extracted from recordings of couples therapy interactions to predict the success or failure of the couples' marriages. In addition, the researchers explored behavioural codes rated by human experts as a feature of marital outcome prediction. The acoustic features were extracted for each speaker individually across the sessions. They extracted several acoustic features, such as speech prosody (pitch and energy), voice quality (jitter, shimmer) and spectral envelope characteristics which are Mel Frequency Spectrum Coefficients (MFCCs). Then, several functionals were computed, such as mean, minimum and maximum. The results showed that the acoustic features outperformed the behavioural codes in predicting the state of the marriage [18]. Another study analysed a corpus of speech recorded during psychotherapy focused on tackling unresolved anger towards an attachment figure. The recordings were from therapy sessions of 22 women; 283 stimuli were extracted and evaluated for emotional content by 14 judges. The emotions were rated dimensionally according to three scales: activation, valence and dominance. The features used for classification were acoustic features representing several prosody components: Fundamental frequency (F0), intensity, duration and voice quality. The automatic classification results showed that the acoustic features were better at predicting activation comparable with valence and dominance such that the features based on F0 were the dominant features [19]. In a recent study related to competency, automatic nonverbal analysis of a presenter's competency and behaviour was conducted based on real-world audiovisual recordings [20]. The presentation competency rating consisted of six items: addressing the audience, structure, language use, body language and voice, visual aids and content credibility. The research used several modalities to extract features: speech (eGeMAPS), facial (head pose, gaze direction and facial action units) and body poses (the estimated locations of body joints). They found that acoustic features transcended face and body pose features in both classification and regression tasks. The study highlighted that speech features were found to be the most dominant nonverbal cues used to estimate presentation competence. Due to the high correlation between emotions and competency, especially in the rating criteria specified for the treatment competency raters, the use of acoustic features is selected as an appropriate approach for detecting the practitioner's competence. The eGeMAPS and BoAWs are considered from the two feature sets that show reliable results in the field of time-continuous recognition of dimensional emotions [5; 21; 22; 23; 24]. Several studies have explored the use of linguistic features in the field of therapeutic alliance. A study examined how to indicate the correlation between emotional elaboration and therapeutic alliance within a single session [25]. In the study, they allocated 40 patients with varying diagnoses to be videotaped, transcribed and analysed using linguistic measures of the referential process using the Discourse Attributes Analysis Program (DAAP), followed by a human-centred scoring with the Working Alliance Inventory (WAI) for every 5-min interval.