## 3 Methods  
### ASR  
Speech recognition is considered a particular form of pattern recognition. The optimal goal of ASR is to find the word sequence given the input speech signal [39]. The success of ASR systems depends on the quality of the system's modelling is. Kaldi is an open-source speech recognition toolkit developed in C++. It provides several recipes for creating a ASR systems by utilising famous databases such as LibriSpeech and WSJ [40]. For the acoustic modelling part in ASR, the main extracted features are Mel filterbanks and MFCCs. They are extracted from window frames of 25 ms with a shift of 10 ms. In addition, 100-dimensional i-vectors has extracted capturing speaker and environment information (noise).  
The LibriSpeech recipe has been adopted for the training of the ASR system. The recipe enhances the acoustic model training by introducing further acoustic models. The language model has been built using the SRI Language Modeling (SRILM) toolkit as advised in Kaldi [40]. Kaldi requires specific data preparation for the sessions' recordings and transcriptions. For that reason, several data processing stages are implemented to create the files required by the Kaldi toolkit. This includes reviewing the alignment start and end times for each segment, checking for any faults in the transcriptions' syntax, introducing Kaldi reserved words, transforming the text to upper case and removing all the punctuation marks from the text. The reserved words used in Kaldi for this experiment are <UNK >for unknown words; <NOISE >for the background noise, such as door slam; and <SPOKEN _NOISE >for non-verbal vocalisation, such as laugh and cry.  
The GSHTS ASR system has been trained using the 54 manually transcribed sessions (as described in Section 2) using the LibriSpeech recipe, and involving Hidden Markov Model with Gaussian Mixture Model (HMM-GMM) and TDNN models plus transfer learning. Using the weight transfer method, a transfer learning approach has been applied to adapt the LibriSpeech model to the GSHTS dataset. Following the LibriSpeech recipe, the LibriSpeech corpus has been used to train a base TDNN acoustic model. High-dimensional MFCCs features have been extracted from the GSHTS dataset and 100-dimensional i-vectors calculated based on the same model trained for the LibriSpeech model. The LibriSpeech model has been applied to generate frame-level acoustic alignments for training and the weight have been copied from the LibriSpeech model to initialise the target model. The acoustic model has been adapted to the acoustics of the GSHTS dataset such that both structure and weights have been transferred from the training of the LibriSpeech corpus. Afterwards, the training stage is conducted using two epochs on the training set. The language model used for the transfer learning approach has been trained as a 4 g with Turing smoothing interpolated with the 4 g language model from the LibriSpeech corpus following the technique used in [41; 42], who reported that the best performance is gained with 60% weight for the training set and 40% weight for the LibriSpeech language model. Further system validation has been implemented using K-fold cross-validation such that the GSHTS dataset has been organised into six equal folds, each containing nine sessions, on account of the relatively small overall dataset size. The micro-average WER has been estimated for the cross-validation considering the number of word imbalances between thedifferent folds. The final model has been used for generating the transcriptions used by the wider system for automatically detecting the treatment competency in the sessions.