• Ms. Jenifer Shylaja M
• MADHESH G
• PABOLU DURGA PRASAD
• RANGITH R R

In this paper, we present a novel Language Augmented Virtual Assistant (LAVA) designed to provide advanced natural language understanding and interaction capabilities akin to industry-standard virtual assistants like Google Assistant and Alexa. Our LAVA system integrates cutting-edge technologies, including convolutional neural networks (CNNs), to augment traditional language processing with visual comprehension. By leveraging CNNs, our virtual assistant gains the ability to interpret and respond to user queries not only through textual input but also through visual cues, enhancing user experience and expanding the range of tasks it can handle. We detail the architecture and design principles of our LAVA system, emphasizing the integration of CNNs for visual processing alongside established natural language processing techniques. Through a comprehensive evaluation, we demonstrate the effectiveness of our approach in achieving high accuracy and responsiveness across various tasks and user scenarios. Our research contributes to the advancement of virtual assistant systems by showcasing the utility of CNNs in enhancing language understanding and interaction capabilities. We anticipate that our work will inspire further exploration and development of multi-modal virtual assistant platforms, driving innovation in human-computer interaction and AI-driven assistance systems

Language Augmented Virtual Assistant (LAVA), CNN, Machine Learning, NLP, Artificial Intelligence, Human-Computer Interaction (HCI), AI Assistant.

"LAVA Language Augmented Virtual Assisstant", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.h37-h46, June-2024, Available :http://www.jetir.org/papers/JETIR2406706.pdf

"LAVA Language Augmented Virtual Assisstant", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pph37-h46, June-2024, Available at : http://www.jetir.org/papers/JETIR2406706.pdf