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High-Performance Computing (HPC) is increasingly being used in traditional scientific domains as well as emerging areas like Deep Learning (DL). This has led to a diverse set of professionals who interact with state-of-the-art HPC systems. The deployment of Science Gateways for HPC systems like Open On-Demand has a significant positive impact on these users in migrating their workflows to HPC systems. Although computing capabilities are ubiquitously available (as on-premises or in the cloud HPC infrastructure), significant effort and expertise are required to use them effectively. This is particularly challenging for domain scientists and other users whose primary expertise lies outside of computer science. In this paper, we seek to minimize the steep learning curve and associated complexities of using state-of-the-art high-performance systems by creating SAI: an AI-Enabled Speech Assistant Interface for Science Gateways in High Performance Computing. We use state-of-the-art AI models for speech and text and fine-tune them for the HPC arena by retraining them on a new HPC dataset we create. We use ontologies and knowledge graphs to capture the complex relationships between various components of the HPC ecosystem. We finally show how one can integrate and deploy SAI in Open OnDemand and evaluate its functionality and performance on real HPC systems. To the best of our knowledge, this is the first effort aimed at designing and developing an AI-powered speech-assisted interface for science gateways in HPC.