Papers
3
Total Citations
14
H-Index
2
About
Lei Xie is a researcher whose work spans computer vision, speech technology, and machine learning, with contributions that bridge theoretical innovation and real-world application. His early work in video surveillance demonstrated a sophisticated approach to multiple pedestrian tracking, employing couple-states Markov chain modeling combined with semantic topic learning — a method that addressed one of the most persistent challenges in intelligent monitoring systems. This paper, his most cited work with 10 citations, reflects his ability to integrate probabilistic modeling with scene understanding in complex visual environments. More recently, Xie has turned his attention to spoken language technology, co-organizing the IEEE SLT 2021 Alpha-Mini Speech Challenge, a community-driven initiative designed to advance deep learning research in keyword spotting and sound source localization specifically for humanoid robots. This work underscores a broader commitment to open-science principles, providing accessible datasets and evaluation benchmarks that have catalyzed measurable progress across the research community. The challenge's focus on robot-centric speech processing reflects an emerging and practically significant frontier in human-robot interaction. Across these domains, Xie's contributions reveal a researcher consistently oriented toward solving applied problems with rigorous computational methods.
Research Focus
Key Achievements
Top Papers
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