## 6 Conclusions  
The automated framework for mouth area segmentation proposed in this paper can reliably analyze the speaker's face. We developed and verified a multistage approach to detecting and segmenting articulators during pronunciation. The framework involves two up-to-date models: YOLO for the detection and DeepLab v3+ for the segmentation, both adjusted in the experiments. We designed a semi-supervised learning workflow to train the segmentation model and assessed all intermediate stages. The method proved its robustness and speed over an extensive database in different conditions. The mouth area was detected with the average precision and F1 score both at 0.999, while the Dice coefficient in the segmentation reached 0.88, 0.86, 0.83, and 0.94 in lips, teeth, tongue, and mouth, respectively. The study is ready to move towards investigating relationships between the image indicators, acoustic cues, and speech therapy diagnosis.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14167146/s1](https://www.mdpi.com/article/10.3390/app14167146/s1)]([https://www.mdpi.com/article/10.3390/app14167146/s1](https://www.mdpi.com/article/10.3390/app14167146/s1)).  
Conceptualization, A.S. and P.B.; methodology, A.S.; software, A.S.; validation, A.S. and P.B.; formal analysis, A.S. and P.B.; investigation, A.S. and P.B.; resources, A.S. and P.B.; data curation, A.S. and P.B.; writing--original draft preparation, A.S.; writing--review and editing, P.B.; visualization, A.S. and P.B.; supervision, P.B.; project administration, P.B.; funding acquisition, P.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by he National Science Centre, Poland, research project No. 2018/30/E/ST7/00525: \"Hybrid System for Acquisition and Processing of Multimodal Signal in the Analysis of Sigmatism in Children\".  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Bioethics Committee for Scientific Research at the Jerzy Kukuczka University of Physical Education in Katowice, Poland (Decision No. 3/2021, issued 25 February 2021).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SLP & Speech and language pathologist \\ CASD & Computer-aided speech diagnosis \\ CAD & Computer-aided diagnosis \\ ROI & Region of interest \\ CLAHE & Contrast-limited adaptive histogram equalization \\ DRLSE & Distance-regularized level set evolution \\ CNN & Convolutional neural network \\ YOLO & You only look once \\ DLv3+ & DeepLabv3+ \\ IoU & Intersection-over-union \\ AP & Average precision \\ PvR & Precision vs. recall curve \\ DSC & Dice similarity coefficient \\ Acc & Accuracy \\ \end{tabular}