## 1 Introduction  
The automatic detection of any human behaviour in recorded speech requires analysis of the speech and language for important features that can accurately detect the chosen behaviours. The speakers' expressed behaviours might be revealed in their speech (acoustics) or in their language use. Representing these by acoustic and language-based features could allow for the modelling and detection of several human behaviours automatically. The acoustic features represent the physical characteristics of the speech signal, such as the loudness, frequency and amplitude of the signal [1]. The language features are related to the speakers' use of language. They are extracted from the text transcripts and can be lexical, semantic or syntactic in nature [2]. Treatment competence during any psychological intervention refers to the levels of the clinician's knowledge of the disorder and the intervention being delivered, the skill with which the intervention is delivered, consideration of how appropriate the intervention is for the problems being addressed [3] and that the model-specific change methods are delivered to the levels required for the patient to make the expected clinical progress [4]. The delivery of competent treatment depends on the understanding and interpretation of communicative behaviours observed by the therapists or practitioners in the sessions. In making these decisions, the therapist or the practitioner could search for different signs in the conversation, such as acoustic and linguistic cues. In the case of psychological interventions, usually therapists or practitioners record sessions with patients using an audio or video recorder. These recordings are useful for clinical supervision and training purposes. However, this procedure is time-consuming and costly to do manually and it is generally difficult for the practitioner to accurately observe some acoustic signs in the moment of a session, such as tracking a patient's pitch or speaking rate, due to the multiple demands of the situation. Detecting these types of cues using automatic speech processing could potentially be more efficient and accurate and so contribute to better clinical supervision and effective and safe delivery of interventions. Such a system would leverage recent progress and maturing of signal processing and machine learning algorithms that are widely applied to recognise human-centred information based on features extracted from audio signals [5, 6, 7]. Psychotherapeutic competence is one of the behaviours that can influence the quality of therapy delivered and this is manually rated via the manual analysis of recordings of sessions and the use of observational rating scales (i.e., typically by trained raters such as clinical supervisors or clinical trainers). Competency concerns the standard of delivering a psychological treatment in a theoretically consistent matter to achieve its expected therapeutic effects [8]. Competency ratings are made based on the presence and quality of certain pre-determined features and an example of this is the Low-Intensity Cognitive Behavioural Therapy (LI-CBT) treatment competency scale [9]. The LI-CBT treatment competency scale is a valid and reliable, observational and scaled measure used for rating practitioners during treatment sessions. The scale consists of six items including: focusing the session, enabling engagement within the guided self-help approach, interpersonal competencies, ability to gather information: specific to change, use of self-help change methods and shared decision making [10]. Low-intensity psychological interventions are designed for mild-to-moderate intensity psychological disorders (typically anxiety and depression). They are defined as being brief in duration and utilising psychoeducational materials to facilitate change within a sound therapeutic alliance. Low-intensity psychological interventions are often delivered on the telephone for ease of access.