## 1 Introduction
The results showed that if the patients' ratings indicated more emotional engagement with their experience and were followed by an experience reflection by mid-session, those patients would have higher scores in the therapeutic alliance by the final part of that same session. Recent work has investigated the connection between non-verbal emotional experiences and how verbal language is affected by ruptures during treatment [26]. They employed linguistic measures of the referential process in association with measures of the therapeutic alliance. A scored measure was utilised to identify ruptured from non-ruptured segments in 27 psychotherapy sessions. The classified segments were scored based on the key linguistic dimensions of the referential process. The results showed that, during ruptured segments, the patients showed a decrease in emotional engagement, an increase in negation compared to non-ruptured segments and an increase in a measure of distancing. The practitioners showed similar patterns to the patients during rupture, in addition to self-disclosure, increased attempts at emotional control, increased references to bodily experiences and increased natural affect words. From the recent studies that helped identify the psychological orientation related to COVID-19, a study focused on recent COVID-19 tweets to apply for sentiment classification works, especially in the Nepali language. They proposed different CNN models for the sentiment classification of Nepali tweets using other feature extraction methods. The proposed methods showed stable and promising classification performance after being validated against traditional machine learning and state-of-the-art techniques [27]. It is well known that the use of linguistic features could complement the use of acoustic features in classifying or predicting common observations in the medical field [28; 29]. Overall, the studies highlighted in this section demonstrated the beneficial effects of the acoustic and linguistic features and their fusion in the counselling domains. In this paper, to predict the competency measure, the fusion of the acoustic features (eGeMAPS and BoAWs) and the linguistic features (DAAP) has been investigated as a complete feature set for the classification and prediction model. Several research studies in psychotherapy tend to employ Automatic Speech Recognition (ASR) to investigate automatic approaches to detect measures related to the quality of therapy and therapeutic outcomes. The recognition accuracy is affected by several characteristics such as the rate of speech, interpretations of prosodic features presented in the pitch, power, intonation, stress, speaker age, and variations in pronunciation. It also can be affected when the same speaker speaks the same word [30]. Due to the challenges concerning the nature of the recordings of therapy sessions and motivational interviewing sessions, the performance of ASR systems in those areas is still considerably lower with ranges reported around 30-40% Word Error Rate (WER) compared to other ASR systems trained on controlled recording conditions with a large training data size [31]. Several studies in the literature confirmed these results, such as a study that investigated an automatic system for rating the therapist's empathy in 200 Motivational Interviewing (MI) sessions for drug and alcohol counselling with human ratings of counsellor empathy [32]. They used ASR (Kaldi adaptation) to transcribe sessions and the resulting words were used in a text-based predictive model of empathy. For training the ASR, they used 1200 therapy transcripts to help define the typical vocabulary and language use. The ASR results gained a mean WER of 43.1%. Another study developed an automatic pipeline that transcribed 225 Cognitive Behaviour Therapy (CBT) sessions' recordings and extracted linguistic features for behavioural coding of the sessions [33]. They adopted an ASR system based on the Kaldi pipeline. The reported WER in the study was 44.01%.