## 1 Introduction  
Speech disorders form a significant social problem as they can mean the individual is alienated, rejected by peers, or handicapped in the labor market [1]. The earlier speech therapy starts, the higher the chance for recovery. Thus, proper screening and treatment seem essential in children as young as four or even earlier [2]. Conventional therapy highly depends on speech and language pathologists' (SLPs) availability. As this remains an issue in many countries, e.g., in rural areas in the United States [3], Australia [4], or Poland [5], each attempt to provide an advanced speech pathology screening tool is welcome. One way to do so is to use remote consultations with the SLP. Such solutions have become more popular recently, also due to the restrictions related to the COVID-19 outbreak [6, 7]. However, the development of automated or semi-automated computer-aided speech diagnosis (CASD) methods is also an option, with access to reliable and fast telemedicine solutions and personal devices enabling easy audio and video data acquisition. CASD follows the computer-aided diagnosis (CAD) concept in medicine, focusing on speech-related areas [8]. CAD systems use advanced computer methods to analyze and process medical data. An extensive subset of CAD employs medical imaging in terms of detection,classification, segmentation, or recognition [9; 10; 11]. Depending on the task, multimodal systems are increasingly expected to make the diagnosis as objective as possible [12]. There has been continuous research interest in CAD development for a long time due to its proven effectiveness and positive impact on specialist work [12; 13]. CASD systems use speech-related data to support experts in decision making through a numerical description of cases, visualizations, suggestions of disorders, etc. Diagnosis based on visual observation by an SLP requires experience, yet specific patterns are difficult to notice and differ between speakers. CASD tools might indicate areas for in-depth examination or reassume specialists in the decision and possible direction of therapy. The speech diagnosis relies mainly on the articulators' observation (lips, tongue, mandible, and soft palate). Recent attempts to support the examinations and describe the articulation phenomena in normative and disordered speech involve various data types. Some offer high spatial and temporal accuracy for the price of invasiveness or considerable experimental requirements or cost. This refers to, e.g., the electromagnetic articulography [14; 15] used to track the articulators in the alternating magnetic field, electropalatography [16; 17] monitoring the tongue-palate contact during pronunciation, or magnetic resonance imaging [18; 19]. Many researchers intuitively employ the audio signal recorded in various setups by single or multiple microphones [20; 21; 22; 23; 24; 25]. The idea of using a non-invasive and readily available video of relevant face regions is another attempt to support the SLP's work. Solutions proposed in this paper follow strict recording protocols, providing repeatable circumstances and results. However, with today's development of robust image analysis algorithms driven by deep learning concepts, implementing our findings into less standardized recordings, e.g., from remote diagnostic consultations, can be considered a natural step further. Recent studies cover both synchronous and asynchronous remote speech therapy approaches. The synchronous mode mainly refers to the video conferences of a therapist and a patient using laptops or smartphones. Such remote therapy is considered comparable to the stationary examination [3; 26; 27; 28]. The asynchronous mode is more useful in treatment than diagnosis. It uses dedicated web platforms to provide patients with online exercises prepared by the SLP based on their prior diagnosis [29; 30; 31].