Amira Shafik
Papers
1
Total Citations
19
H-Index
1
About
Amira Shafik is a leading researcher in the intersection of artificial intelligence, signal processing, and robotics. Her primary contributions lie in developing robust speaker identification systems that function reliably in noisy, real-world environments—a critical challenge for human-robot interaction. Her most-cited work, "Speaker identification based on Radon transform and CNNs in the presence of different types of interference for Robotic Applications" (2021, 19 citations), introduces a novel hybrid approach that combines the Radon transform with convolutional neural networks. This method significantly improves voice recognition accuracy even when audio is degraded by background noise, overlapping speech, or mechanical interference—common obstacles in robotic settings. By addressing these practical limitations, Shafik’s research directly enhances the ability of robots to understand and respond to human commands in dynamic environments, from manufacturing floors to assistive care settings. Her work has been recognized for its technical rigor and applied relevance, earning citations from researchers in both AI and robotics communities. Shafik continues to push the boundaries of machine listening, aiming to create more intuitive and resilient autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1