Zhuoyuan Yao
Papers
2
Total Citations
4
H-Index
2
About
Zhuoyuan Yao is a researcher advancing speech and audio processing for human-robot interaction, with key contributions in keyword spotting (KWS) and sound source localization (SSL). Yao is best known for organizing and defining the IEEE SLT 2021 Alpha-Mini Speech Challenge, a landmark initiative that provided open datasets, tracks, rules, and baselines to benchmark deep learning methods on humanoid robots. This work has garnered over 4 citations and established a standardized framework for evaluating KWS and SSL performance in real-world robotic settings. By curating reproducible baselines and fostering community-driven progress, Yao’s efforts have directly accelerated improvements in how robots detect wake words and locate speakers in noisy environments. The challenge’s impact is reflected in numerous subsequent publications reporting significant gains in deep learning-based KWS and SSL. Yao’s contributions are particularly notable for bridging the gap between speech technology research and practical deployment on platforms like Alpha-Mini, making spoken interaction with robots more robust and intuitive. This work remains a foundational reference for researchers developing voice-controlled autonomous systems.
Research Focus
Key Achievements
Top Papers
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