## 1 Introduction
In both cases, there is great potential for collecting data to design tools for computerized speech and hearing diagnosis methods, including initial assessment of selected disorders [20; 32; 33; 34; 35; 36; 37]. Additionally, the SLPs often use smartphone-recorded videos during the in-person examinations to document their findings, enable later verification, or monitor the therapy progress. Even though such hand-held recordings can be labile or have low quality in audio or video, they still can be considered helpful in automated diagnosis support. The non-invasiveness and easy application of audio and video recording open perspectives for hybrid frameworks. Each source delivers data with its own indicators of speech disorders. If synchronized and merged, they can improve the inference and eventual diagnosis [38]. The hybridization of input data and their confrontation with SLP annotations of normal and disordered articulation features support the underlying goal of our research project: finding, describing, and exploiting knowledge on the relationships between articulation, acoustics, and face imaging in preschool children. Here, we focus on image analysis to accurately detect and segment relevant articulators: lips, teeth, tongue, and the whole mouth. Segmentation of articulators is the essential step towards a quantitative description of visual aspects of pronunciation. We believe monitoring the mouth area dynamics can bring valuable indicators of speech disorders. It can be performed by using object features describing the appearance, shape, size, or texture of articulators, which leads us back to the issue of reliable segmentation. The visual findings can essentially enrich the CASD research, which relies mainly on the acoustics so far. We aim to prepare extensive data collection and methods to link face imaging, acoustics, and articulation SLP diagnoses. Visual face observation and analysis methods are present in both scientific literature and commercial solutions. The range of motives covers automatic speech recognition and lip reading, computer-aided strategies for dental issues, rehabilitation, or health monitoring purposes. Just a few works concern computer-aided speech diagnosis and therapy. Canonic studies on mouth or lip segmentation investigated various state-of-the-art methods, e.g., expectation-maximization-based unsupervised clustering [39], shape-based fuzzy clustering [40; 41], or multi-scale wavelet edge detection [42]. Several valuable studies have been proposed for mouth/lip segmentation in the last three years, mostly taking advantage of deep learning concepts. None of them were dedicated directly to supporting child speech pathology diagnosis, yet the scope of the analysis is somehow coherent with our goals. [PERSON] et al. [43] used a deep architecture to segment the infrared thermal image of the entire face into nine classes, including the mouth. Their framework was based on a multiclass U-Net encapsulated by the conditional generative adversarial network (cGAN). The average mouth segmentation intersection-over-union (IoU) was below 0.7 (corresponding to a Dice similarity coefficient of ca. 0.82). Two recent studies focused on video-based word recognition from lip motion. [PERSON] and [PERSON] [44] proposed a multistage lip motion detector as a part of a framework for classifying Amharic words. They used machine learning tools for classification, yet the segmentation relied on more traditional methods: Viola-Jones-based object detection, contrast enhancement, Sobel edge detection, and thresholding. The classification was based on a set of hand-crafted geometric and hybrid features. The overall accuracy over a collection of 14 words reached 66.43% using a support vector machine (SVM); the study does not report mouth segmentation efficiency. [PERSON] et al. [45] designed a spatiotemporal model for lip segmentation in a sequence of video frames using a combination of CNNs and bi-directional gated recurrent units (Bi-GRUs).