Development and Testing of Voice User Interfaces Based on BERT Models for Speech Recognition in Distance Learning and Smart Home Systems

Voice User Interfaces (VUIs) focus on their application in IT and linguistics. Our research examines the capabilities and limitations of small and multilingual BERT models in the context of speech recognition and command conversion. We evaluate the performance of these models through a series of experiments, including the application of confusion matrices to assess their effectiveness. The findings reveal that larger models like multilingual BERT theoretically offer advanced capabilities but often demand more substantial resources and well-balanced datasets. Conversely, smaller models, though less resource-intensive, may sometimes provide more practical solutions. Our study underscores the importance of dataset quality, model fine-tuning, and efficient resource management in optimising VUIS. Insights gained from this research highlight the potential of neural networks to enhance and improve user interaction. Despite challenges in achieving a fully functional interface, the study provides valuable contributions to the VUIs development and sets the stage for future advancements in integrating AI with linguistic technologies. The article describes the development of a voice user interface (VUI) capable of recognising, analysing, and interpreting the Ukrainian language. For this purpose, several neural network architectures were used, including the Squeezeformer-CTC model, as well as a modified w2v-bert-2.0-uk model, which was used to decode speech commands into text. The multilingual BERT model (mBERT) for the classification of intentions was also tested. The developed system showed the prospects of using BERT models in combination with lightweight ASR architectures to create an effective voice interface in Ukrainian. Accuracy indicators (F1 = 91.5%, WER = 12.7%) indicate high-quality recognition, which is provided even in models with low memory capacity. The system is adaptable to conditions with limited resources, particularly for educational and living environments with a Ukrainian-speaking audience.