Papers
2
Total Citations
18
H-Index
2
About
Dinh-Son Le is a researcher at the forefront of human-robot interaction and multimodal AI, specializing in computer vision, speech recognition, and affective computing. His work bridges the gap between machines and human social cues, enabling more intuitive and personalized robotic systems. Le’s most impactful contribution, **KFSENet** (14 citations), introduces a key frame-based skeleton feature estimation network that integrates action recognition with face and emotion analysis, allowing social robots to perceive and respond to human gestures and expressions in real time. This work directly advances robot vision for applications in healthcare, assistive technology, and collaborative environments. Additionally, Le addresses the critical challenge of speech technology inclusivity with his 2023 paper on improving speech recognition accuracy for non-native English speakers. By developing a novel error correction module combining Bag-of-Words and deep neural networks, his research reduces bias in cloud-based speech-to-text services, making voice interfaces more accessible to global users. Through these contributions, Le demonstrates a commitment to creating AI systems that are both socially aware and linguistically equitable, laying groundwork for robots that truly understand and adapt to diverse human users.
Research Focus
Key Achievements
Top Papers
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