2025 Asian Conference on Communication and Networks (ASIANComNet 2025)

Voice assistants have become extremely effective interfaces that connect machine operation and human communication. Though their usefulness is frequently restricted to mobile devices, Internet of Things appliances, or cloud-dependent ecosystems, existing implementations show the promise of speechdriven interaction. Conventional assistants made for computer tasks have limited capabilities, inadequate support for multiple languages, and little personalization. In this work, we introduce an AI voice assistant for computers that combines multimodal interaction, transformer-based natural language processing, and sophisticated speech recognition. Complete systemlevel commands, such as file management, software control, and productivity task automation, can be carried out by the suggested assistant. In contrast to previous research, our model uses a hybrid edge–cloud architecture to minimize latency while maintaining security through voice biometric user authentication and local data handling.While gesture recognition expands interaction beyond voice alone, context awareness and multilingual processing improve accessibility for a wide range of users. When compared to current IEEE models, performance evaluation shows gains in word error rate, latency, and task success rate. The suggested framework emphasizes how next-generation voice assistants have the potential to revolutionize computer interaction in both personal and professional computing environments by making it more efficient, safe, and natural.

Biometric security,Edge computing；,Whisper ASR,Natural Language Processing,Voice Assistants,Multimodel Interaction