Chiyomi Miyajima
Papers
2
Total Citations
11
H-Index
2
About
Chiyomi Miyajima is a leading researcher in autonomous driving systems and human-robot interaction, with a focus on end-to-end navigation and interactive speaker identification. Her pioneering work on "End-to-End Navigation with Branch Turning Support Using Convolutional Neural Network" (2018, 9 citations) addresses a critical gap in autonomous driving research by developing methods that generate control signals directly from external sensors, moving beyond traditional lane-keeping approaches to enable complex maneuvers like branch turning. This work has significant implications for real-world autonomous vehicle navigation in urban environments. Additionally, her research on "Minimum Classification Error Interactive Training for Speaker Identification" (2006, 2 citations) introduces an innovative online discriminative training algorithm that allows robots to incrementally acquire and recognize speakers' voice characteristics during natural interaction, without requiring explicit user identification. This contribution is foundational for developing more intuitive and adaptive human-robot interfaces. Miyajima's work bridges the gap between autonomous navigation and interactive systems, demonstrating how deep learning and discriminative training can create more responsive and intelligent autonomous agents that can both navigate complex environments and interact naturally with humans.
Research Focus
Key Achievements
Top Papers
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- 2