Zhanna Deineko
Papers
1
Total Citations
6
H-Index
1
About
Zhanna Deineko is a researcher whose work sits at the intersection of artificial intelligence, robotics, and human-computer interaction. Her primary focus is on developing intelligent systems that can understand and respond to human speech, with a particular emphasis on voice control for robotic platforms. Deineko’s most cited paper, “Building robot voice control training methodology using artificial neural net” (2017, 6 citations), outlines a foundational methodology for training neural networks to interpret spoken commands, enabling more intuitive and accessible robot operation. This contribution is significant for advancing natural language interfaces in automation and assistive technologies. While her citation count reflects the niche, emerging nature of her field, Deineko’s work is notable for its practical, hands-on approach to bridging the gap between complex AI models and real-world robotic applications. Her research is especially relevant for students and engineers interested in the challenges of speech recognition, machine learning, and the design of user-friendly robotic systems.
Research Focus
Key Achievements
Top Papers
- 1