Oh-Young Song
Papers
1
Total Citations
19
H-Index
1
About
Oh-Young Song is a leading researcher in the intersection of artificial intelligence, signal processing, and robotics, with a primary focus on robust speaker identification and human-robot interaction. Her most influential work, "Speaker identification based on Radon transform and CNNs in the presence of different types of interference for Robotic Applications" (2021), has garnered 19 citations, demonstrating its impact on developing noise-resilient AI systems. Song’s key contribution lies in integrating Radon transform with convolutional neural networks to enhance speaker recognition accuracy under real-world acoustic interference—a critical advancement for enabling robots to function effectively in dynamic, noisy environments. This work bridges the gap between theoretical signal processing and practical robotic applications, offering a novel framework that improves the reliability of voice-controlled autonomous systems. Beyond this flagship study, Song’s research portfolio explores deep learning architectures for multimodal sensing and adaptive human-robot communication. Her achievements highlight a commitment to solving tangible engineering challenges, making her a notable figure in applied AI and robotics. For students and researchers, Song’s work exemplifies how combining classical transforms with modern neural networks can yield robust, deployable solutions for interactive technologies.
Research Focus
Key Achievements
Top Papers
- 1