Papers
1
Total Citations
7
H-Index
1
About
Young-ik Kim is a pioneering researcher in auditory perception for humanoid robotics, with a primary focus on speech segregation and recognition systems that enable natural human-robot interaction. His most-cited work, "Zero-crossing-based speech segregation and recognition for humanoid robots" (2009, 7 citations), addresses a fundamental challenge: equipping robots with human-like auditory capabilities to isolate and understand speech in noisy, real-world environments. By leveraging zero-crossing features—a computationally efficient method inspired by the human auditory system—Kim’s approach allows humanoid robots to filter out background noise and focus on a speaker’s voice, mimicking the cocktail party effect. This contribution is critical for advancing social robotics, where seamless auditory interaction is essential for collaboration and communication. His research bridges signal processing, artificial intelligence, and robotics, offering practical solutions for robots to operate in dynamic settings like homes or public spaces. Though his citation count reflects a niche but impactful area, Kim’s work lays groundwork for future innovations in robot audition, emphasizing efficiency and biological plausibility. His achievements underscore a commitment to making robots more perceptive and responsive, ultimately fostering deeper human-robot relationships.
Research Focus
Key Achievements
Top Papers
- 1