UEQManager: A Non-intrusive and Real-time System for Recognizing and Managing UEQ in Multilingual Voice Assistants

User experience quality (UEQ) plays a critical role in multilingual intelligent voice assistant (VA), where real-time feedback directly affect interaction experience. However, current UEQ evaluation methods primarily depend on surveys, posing limitations for dynamic and user-centered adaptation. We propose a non-intrusive real-time system, UEQManager, for automated UEQ recognition and management. First, UEQManager incorporate interpretable deep learning models to predict UEQ in seven subdimensions. Second, leveraging LLM and expert focus group, we design adaptive interaction interface based on recognized UEQ states. Third, we implement integration system capable of real-time, non-intrusive UEQ recognition and management. We validate the effectiveness of UEQManager through a user testing experiment. Results reveal that UEQManager significantly outperformed baseline, yielding an average UEQ improvement of 27.29% over baseline and demonstrating statistical significance across all subdimensions. This work contributes a proof of concept HCI system that translates webcam gaze cues into adaptive design decisions for multilingual VAs and illustrates how designers can couple interpretable sensing with proactive interaction design.