Amir Gholipour
Papers
2
Total Citations
8
H-Index
2
About
Amir Gholipour is a researcher at the forefront of integrating artificial intelligence with human-robot interaction, specializing in automated lip-reading systems. His work centers on developing deep learning architectures—particularly Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks—to enable robots to interpret spoken words from visual lip movements. Gholipour’s major contributions include the creation of a robotic system capable of automatically recognizing Persian words through lip-reading, a breakthrough that bridges language-specific challenges with real-time machine perception. His most cited papers, "Automatic Lip Reading of Persian Words by a Robotic System Using Deep Learning Algorithms" (2024) and "Automated Lip-Reading Robotic System Based on Convolutional Neural Network and Long Short-Term Memory" (2021), each have garnered 4 citations, reflecting their foundational role in this niche yet impactful field. By advancing non-auditory speech recognition, Gholipour’s research holds promise for assistive technologies, silent communication interfaces, and human-robot collaboration in noisy environments. His work exemplifies how deep learning can transform subtle visual cues into actionable robotic responses, marking him as a key contributor to the evolving landscape of intelligent, perceptive machines.
Research Focus
Key Achievements
Top Papers
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