## 3 Methods
### Automatic Detection of the Treatment Competency
#### 3.2.1 Feature Extraction  
The extracted features consists of acoustic and linguistic features for each speaker individually. The acoustic features extracted are eGeMAPS and BoAWs using the same settings described in [15]. The eGeMAPS comprises 88 features covering the following acoustic features: spectral, cepstral, prosodic and voice quality information. Those feature groups are presented using a set of descriptors called Low-Level Descriptors (LLDs). The spectral related LLDs are Alpha ratio, Hammarberg index, Energy slope 0-500 Hz and 500-1500 Hz, and Spectral flux. The voice quality related LLDs are First, Second and Third Formant, Harmonic difference H1-H2, and Harmonic difference H1-A3. The prosodic related LLDs are loudness and F0. The cepstral related LLDs are the MFCCs from 1 to 4. In addition, 13 MFCCs and their deltas and delta deltas are computed using a set of acoustic LLDs. The MFCCs consider the human perception sensitivity at predefined frequencies, which converts the conventional frequency to Mel Scale frequency [43]. The functionals of all the LLDs computed consists of several mathematical representations such as the arithmetic mean and the coefficient of variation. The details of those functionals and the feature set can be found in [44]. The features are extracted using the openSMILE toolkit [45,46]. The BoAWs method involves generating words with a clustering algorithm and quantising the original features to generate the bag-of-words in the form of a histogram. The MFCCs are used in BoAWs as a front end to compute the acoustic features. The process starts by quantisation of the LLD vectors from single frames according to a codebook after the process of extracting the MFCC LLDs from the audio signal. This codebook is a result of a random sampling of the LLDs for all the training partition. Finally, a histogram is generated based on the distribution of the codebook vectors over the whole audio segment for each recording in the test set as a bag-of-words [47]. The BoAWs are extracted using the open-source toolkit openXBOW [47]. Since those features are extracted based on a variable number of frames for each session, majority voting has been applied to extract a single feature vector for each session. The resulting acoustic feature vector consists of 88 eGeMAPS and 100 BoAWs for each speaker, for a total of 376 acoustic features. The linguistic features are extracted based on the Discourse Attributes Analysis Program (DAAP) dictionaries scores. The DAAP is a computer-based text analysis system designed based on the RA model. It is designed to analyse any type of text, including written texts and transcripts of verbal language with any number of speakers. As shown in Figure 2, the RA model that is the base for the used linguistic features consists of three main categories: unweighted dictionaries, weighted dictionaries and dictionary covariations. The weighted dictionaries were developed through a process of modelling the frequency with which words are presented in texts at several phases of RA as scored by human raters, which are as follows [16,48]:  
* The Weighted Referential Activity Dictionary (WRAD) is an RA measure that consists of words identifying moments in language when a speaker is immersed in the narrative such that high WRAD indicates the Symbolising phase in the RA process. * The Weighted Reflection/Recognising List (WRRL) is a measure that assesses the Reflection/Recognising phase in RA. It measures the degree to which a speaker is attempting to recognise and understand the emotional implications of an event or set of events in their own or someone else's life or in a dream or fantasy. * The Weighted Arousal List (WRSL) is a preliminary measure for modelling the Arousal phase in the RA process.