## 1 Introduction
Their approach was also supported by preprocessing for the initial mouth and lip localization through the Haar cascade classifier and hybrid active contours. Again, the authors focused on quantitative word recognition results (accuracy over 90% in a public database of BBC TV segments), limiting the mouth segmentation assessment to some illustrations. [PERSON] et al. [46] used deep architectures to segment lips in two extensive image databases. Based on the experiments over nine different state-of-the-art networks, they selected the Mobile DeepLabV3 model as the core of their methodology. Depending on the test dataset, the lip segmentation Dice index balanced around a notable value of 0.93. On the other hand, the list of studies addressing teeth or tongue segmentation in color images is significantly shorter. Teeth analysis is almost absent in the field, except as a minor part of more general face image research [47; 48], practically without reliable numerical assessment. Tongue segmentation is often conducted in a highly specific area, where the tongue's behavior hardly retains its motion during ordinary speech, e.g., traditional Chinese medicine (TCM) [49; 50; 51; 52]. In such cases, a large part of the tongue has to be clearly visible in the central part of the picture, sticking out of the mouth, and the segmentation IoU may reach 0.97 [50; 52]. Instead, the tongue is mostly hidden behind the teeth or lips during pronunciation. Among individual studies addressing tongue segmentation for speech therapy, the research of [PERSON]'s team seems very promising [53; 54] with the use of deep models like U-Net. Still, their methods for segmenting the tongue, lips, and teeth were not assessed quantitatively, so we can hardly refer to the results. Our preliminary attempt to analyze the tongue [55] was targeted strictly at segmentation in a dedicated setup, using well-illuminated high-resolution videos. Our CNN trained from scratch produced segmentation outcomes highly dependent on the object size (visibility), with a mean IoU reaching 0.74. In this study, we aim to prepare the tool for the automated analysis of images of the speaker's face during pronunciation. Our wearable multimodal data acquisition device containing two cameras can capture the unobstructed frontal view of the extended mouth area from a distance not exceeding 15 cm. A stereovision system can allow a more comprehensive observation of the speaker's articulation organs during pronunciation through 3D scene mapping based on a pair of cameras. Stereoscopic vision reflects the depth of the observed scenes and is how humans perceive reality. To our knowledge, our study [38] is the first report on implementing this idea in the articulation data. It generates a 4 Dmultimodal speaker model based on a 3D point cloud textured with camera images and spatial acoustic signals. The segmentation of articulators can bring additional information to improve the model and provide foundations for quantitative support of the diagnosis. During the recording sessions, we collected a database containing stereo recordings from 76 children aged four to seven (36 girls and 40 boys). With a total of 17,913 single-view frames, we propose a multistage framework to design a model for automated segmentation of selected articulators: lips, teeth, tongue, and the entire mouth region. In the model, we merge two deep networks: YOLOv6 (you only look once) for object detection [56], and DeepLab v3+ for semantic segmentation [57; 58]. Semi-supervised learning of the deep segmentation model is our attempt to overcome the time-consuming problem of preparing ground-truth delineations over an extensive image dataset. It is performed in two steps and involves a dedicated initial segmentation method based on a distance-regularized level set evolution producing weak ground-truth data for the initial training. The network is then fine-tuned using a small portion of data delineated manually by an expert. The segmentation Dice index exceeds 0.83 in each articulator, with a top result of 0.95 in the whole mouth.