Mouldi Bedda
Papers
2
Total Citations
6
H-Index
2
About
Mouldi Bedda is a researcher whose work sits at the intersection of speech recognition, robotics, and intelligent control systems. His primary research focuses on developing robust voice command interfaces for robotic manipulators, with a particular emphasis on Arabic speech recognition in noisy environments. Bedda's major contributions include pioneering the use of Hidden Markov Models (HMM) combined with Kalman filters to create wireless voice command systems capable of controlling five-degree-of-freedom manipulator arms. His 2008 work on Arabic spotted words recognition for didactic manipulators established a foundation for real-time speech-controlled robotics, while his 2009 paper advanced this by implementing Kalman filtering as a noise selector to improve recognition accuracy in challenging acoustic conditions. Though his citation counts (4 and 2 respectively) reflect a specialized niche, his work represents important early steps in integrating Arabic speech processing with robotic control systems. Bedda's research demonstrates how combining statistical signal processing techniques can enable more natural human-robot interaction, particularly for applications requiring hands-free control in industrial or assistive contexts.
Research Focus
Key Achievements
Top Papers
- 1
- 2