Voice Authentication at Scale: A Room Level Approach for Smart Devices

In light of the rapid expansion of voice- activated smart devices, achieving reliable and secure voice authentication across multiple rooms presents complex challenges. Traditional voice authentication methods fall short in differentiating speakers based on their specific locations, raising security risks in shared spaces. This study introduces a room-level voice authentication model that leverages both spatial audio features and machine learning techniques to distinguish users based on their physical environment. By analyzing room-specific acoustic signatures and integrating them with biometric voice characteristics, the proposed approach strengthens access control, offering a scalable solution that addresses the unique demands of multi- room setups. Performance evaluations indicate high accuracy in correctly identifying both speaker and room, confirming the method’s potential for secure, location-based voice authentication in smart homes and office spaces.