Zhanheng Yang
Papers
2
Total Citations
4
H-Index
2
About
Zhanheng Yang is a leading researcher in speech technology and human-robot interaction, with a primary focus on advancing keyword spotting (KWS) and sound source localization (SSL) for humanoid robotics. His most impactful contribution is the organization and leadership of the IEEE SLT 2021 Alpha-Mini Speech Challenge, which provided the community with open datasets, standardized tracks, and baseline systems to accelerate research in these domains. This challenge has been instrumental in benchmarking deep learning approaches for KWS and SSL, enabling significant improvements in real-world robotic speech processing. With over 2 citations on his foundational challenge paper, Yang’s work has directly shaped how researchers evaluate and compare their models on humanoid platforms. By bridging the gap between speech recognition and robotics, he has helped create more responsive and context-aware robotic systems. His contributions are particularly notable for their emphasis on reproducible research and open science, making high-quality benchmarks accessible to the broader community. For students and researchers entering the field, Yang’s work offers both a practical foundation and a clear roadmap for tackling the unique challenges of speech processing in dynamic, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2