Besim Alibegovic

Papers

Total Citations

H-Index

About

Besim Alibegovic is a researcher whose work sits at the intersection of human-robot interaction and speech technology, with a particular focus on making service robots more accessible through natural language. His most cited work, a 2020 performance evaluation of speech recognition systems for a service robot, systematically compares on-device solutions like Kaldi and Mozilla’s DeepSpeech with cloud-based APIs from IBM, Microsoft, and Google. This study provides critical insights into the trade-offs between accuracy, latency, and autonomy in robotic platforms, helping to guide the design of more responsive and reliable human-machine interfaces. With 12 citations, this paper has become a reference point for researchers developing voice-controlled assistant robots. Alibegovic’s contributions are especially valuable in contexts where internet connectivity is unreliable, as his evaluation of on-device ASR systems supports the deployment of robots in real-world, dynamic environments. His work demonstrates a practical, applied approach to bridging the gap between cutting-edge speech recognition and the tangible needs of service robotics, making him a notable figure in the field of intelligent, interactive systems.