## 2 Related Work
### Natural Interactions
A major problem is the limited support for numerous global languages and dialects, many of which are underrepresented in existing language datasets. This underrepresentation can lead to misinterpretation and a lack of inclusivity in VUI applications [56]. In addition, nuances such as context, ambiguity, intonation, and cultural differences pose difficulties for current natural language processing systems. Addressing these issues is critical to the broader adoption and effectiveness of VUIs, especially in multilingual and multicultural environments [57]. Recent advances in VUI technology include emotion recognition, where the system can recognize and respond to the user's emotional tone of voice, and multilingual support, which enables interactions in multiple languages [58]. In two-way voice communication, natural language processing enables the system to understand the meaning of the spoken words, while the speech synthesis system generates responses in the form of human-like speech. Modern VUIs often integrate systems to handle complex conversations with multi-round interactions [59]. Gaze-tracking UIs use eye movements, in particular the direction of the user's gaze, to control or manipulate digital environments. They interpret eye movements and translate them into commands or actions on a screen or in a virtual environment [60]. As hands-free interfaces, they are particularly beneficial for assistive technologies, XR, and public installations where traditional input methods can be impractical [52]. To ensure that the device works as accurately as possible, it must be individually calibrated for each user to calculate the gaze vector [61]. The two main types of gaze-based interaction are gaze pointing and gaze gestures. Gaze pointing uses the user's gaze as a pointer and allows the user to select or interact with objects on a screen without having to physically touch the device. The UI responds to the location on the screen or in space that the user is looking at, often with a cursor or focus indicator [62]. Gaze gestures recognize and interpret complex eye movement patterns such as blinking or switching gaze between targets as commands, enabling a wider range of gestures and interactions [63; 64]. Gaze-recognizing systems can adapt the content or interface depending on where the user is looking [65]. Recent advances, including multimodal approaches that combine gaze with speech or gestures, aim to mitigate these issues and improve the user experience [66] or combine them with technologies that measure brain activity to create a brain-computer interface [67]. Advances in this area aim to improve accuracy and overcome problems with interaction errors such as the \"midas touch\", which occurs when the system's response to a user action and the user's expected outcome of that action do not match [68]. New findings in the field of deep learning have also influenced gaze-tracking technologies, so that more and more authors are working on the application of machine and deep learning in this field. Numerous authors have created and used their own annotated datasets for deep learning, some of which are publicly available, such as the Open EDS Dataset [69] or Gaze-in-Wild [70]. Among the approaches used, convolutional neural networks (CNNs) showed the best results in eye tracking and segmentation [71; 72]. Gesture-based NUIs allow users to interact with devices by using certain gestures, such as hand or body movements or facial expressions, as input commands. These technologies rely on users' body movements to interact with virtual objects and perform tasks, and they rely heavily on the ergonomics associated with various interactions [73]. One form is hand gesture recognition, where systems use cameras and different sensors (e.g., Leap Motion or Kinect) to capture hand movements and interpret them as commands for tasks such as selecting objects or navigating menus [44,74]. Wearables such as data gloves, video cameras, and sensors such as infrared or radar sensors [75] are also used for gesture recognition.