Mohammad Abusheikh
Papers
1
Total Citations
3
H-Index
1
About
Mohammad Abusheikh is a researcher at the forefront of advancing natural language processing and automatic speech recognition (ASR), with a particular focus on low-resource languages. His most-cited work, "Advancing Arabic Speech Recognition Through Large-Scale Weakly Supervised Learning" (2025), tackles a critical challenge in human-machine interaction: building high-performing ASR models for languages like Arabic, which suffer from limited annotated data and complex dialectal variations. By leveraging large-scale weakly supervised learning, Abusheikh proposes a scalable approach that reduces reliance on expensive, manually transcribed datasets, enabling more robust and accessible speech interfaces. This contribution has already garnered 3 citations in its early publication year, signaling growing interest from the ASR community. His research directly impacts applications ranging from conversational agents and automated subtitling to industrial robotics and call center automation. Through his innovative methodology, Abusheikh is helping to bridge the digital language divide, making voice-enabled technology more inclusive for Arabic-speaking populations worldwide.
Research Focus
Key Achievements
Top Papers
- 1