Abstract: Desktop voice assistants have become a popular interface for human-computer interaction because they allow users to speak with their computers in natural language. However, modern desktop voice assistants sometimes struggle to understand and respond to complex queries and context. Privacy concerns concerning the collection and use of user data also remain significant barriers. In order to enhance desktop voice assistants, we present a novel personalization and multimodal interaction technique in this research. By combining text, voice, and visual inputs, our approach improves the accuracy and relevance of the responses. By combining several modalities, our system is able to better understand context and user intent, leading to more meaningful interactions. Furthermore, we propose a user-centric personalization strategy whereby the voice assistant progressively learns each user's preferences and usage patterns. Because it depends less on centralized data collection and processing, this customized approach improves user experience while simultaneously addressing privacy concerns. Through a series of experiments and user surveys, we show how effective our approach is in improving the overall performance and user experience of desktop voice assistants. Our results show that the combination of multimodal interaction with personalization produces more accurate and contextually relevant responses, improving the user experience. All things considered, our research advances desktop voice assistant technology by addressing significant interface and personalization issues. Our approach has the potential to significantly improve desktop voice assistant usability and user acceptance, which could result in considerably more intuitive and natural human-computer interaction.