Van-An Tran

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About

Van-An Tran is a researcher at the forefront of socially-aware robotics and human-computer interaction, with a focus on bridging the gap between machine perception and natural human communication. Their key research areas include robot vision, action recognition, and speech recognition, particularly for non-native speakers. Tran's most notable contribution is the development of KFSENet, a key frame-based skeleton feature estimation and action recognition network that integrates face and emotion recognition into robot vision. This work, which has garnered 14 citations, enables social robots to engage in more personalized and context-aware interactions by simultaneously interpreting human actions and emotional states. In a complementary vein, Tran has also advanced speech recognition technology by proposing an error correction module that uses Bag-of-Words and deep neural networks to improve accuracy for non-native English speakers with foreign accents, a critical step toward inclusive voice interfaces. With a growing citation record and a focus on making AI systems more perceptive and accessible, Tran's work is shaping the future of empathetic and adaptive robotic companions.