## 5 Discussion  
The experiments presented in Section 4 justify our methodology by indicating high segmentation efficiency for the lips, teeth, tongue, and mouth. A reliable face image analysis tool is a solid basis for a more in-depth analysis of speech disorders in children. We can use the obtained regions to extract different features. Since we use stereo video streaming, we can think of a variety of clues derived not only from plain-image texture or shape but also from three-dimensional objects formed by concatenating data from subsequent frames. A stereovision presentation of the data is also possible in the current setup [38]. As our dataset contains corresponding audio data and comprehensive expert SLP annotations, we can now hybridize the video and acoustic information to prepare a multivariate statistical analysis of the possibility of detecting speech disorders based on automated data analysis. That opens perspectives for an efficient and broadly applicable CASD tool, as no similar approaches are reported. Recall that our primary goal in this study was to design robust segmentation supported by effective object detection using an extensive image dataset without a need to perform an exhausting and time-consuming expert delineation of objects under consideration. Therefore, we implemented a semi-supervised learning framework merging machine learning and soft computing concepts. The process follows the rules of art supported by our ideas, e.g., preparing weak labels through DRLSE segmentation over a fuzzified image. The main novelty can be found in (1) segmenting various articulators for CASD, especially tongue and teeth, which have not been studied in this matter, and (2) integrating multi-purpose instances of deep learning and fuzzy computations and proposing a semi-supervised approach over an extensive and diversified dataset to solve significant real-life CASD problems in children. The model meets our expectations regarding quality, but the study brings several issues to the discussion. YOLOv6 offers efficient object detection at levels sufficient for our objectives. The teeth and tongue were moderately difficult to detect. The tongue detection issues are its color (often resembling lips), mostly small area, ambiguous contours, dark conditions inside the mouth, and relatively rare frequency of occurrence. Teeth detection suffers from variability in the completeness of the dentition, distribution of teeth, their sizes and shapes, and being covered by the lips and tongue. However, both articulators were detected accurately enough to prepare a reliable base for initial segmentation. Note that the ultimate model does not rely directly on teeth or tongue detection. On the other hand, lips detection is as essential in regular image segmentation as in training. The results in Section 4.1 indicate practically perfect detection (AP and F1 score at 0.999) with an outstanding fidelity (median IoU of 0.862). Hence, all our goals for object detection were met. We tested and compared multiple versions of YOLO and confirmed YOLOv6 trained with mosaic-type input data was the most robust. A two-step training of the DeepLab v3+ model is justified by the results presented in Figure 11. In all articulators, the step-by-step progress is noticeable and consistent. The rough DRLSE-based segmentation sets the fair starting point for the method: a median DSC from 0.718 in tongue to 0.912 in mouth. These results are significantly raised by intensive weakly-supervised transfer learning using over 16k images with DRLSE-made labels (we can observe a drop in median DSC after the first training stage only in tongue). Fine-tuning using ca. 6% of all frames available for training brings further considerable progress. However, the decision to select the final backbone for our model is not obvious. Figure 12 indicates the tradeoff in segmentation efficiency between the smaller and more irregular objects (teeth and tongue) and the larger lips and mouth.