Jorge Wuth
Papers
8
Total Citations
107
H-Index
4
About
Jorge Wuth is a leading researcher at the intersection of automatic speech recognition (ASR) and human-robot interaction (HRI), with a focus on making robots hear and understand humans in dynamic, real-world environments. His work tackles the fundamental challenge of replacing black-box ASR integration with context-aware systems that model the robot’s environment, user state, and movement. Wuth’s most cited paper (2018, 50 citations) pioneered a DNN-HMM framework that incorporates HRI context directly into the speech recognition pipeline, dramatically improving robustness in interactive scenarios. He has further advanced the field by demonstrating how visual servoing and beamforming can be unified to track moving speakers and suppress time-varying noise and reverberation—key contributions for indoor HRI. His 2020 study on speech technology’s role in user perception (19 citations) highlights the bidirectional link between accurate ASR and user trust. Notably, Wuth has also extended his expertise to healthcare, developing automatic detection of dyspnea (respiratory distress) in real HRI settings (2023). With a growing body of work that bridges signal processing, machine learning, and robotics, Wuth is shaping the future of machines that listen not just to words, but to the context and condition of the humans they interact with.
Research Focus
Key Achievements
Top Papers
- 1DNN-HMM based Automatic Speech Recognition for HRI Scenarios50 citations · 2018
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- 3Automatic Speech Recognition for Indoor HRI Scenarios15 citations · 2021
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- 8Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios2 citations · 2023