Papers
15
Total Citations
191
H-Index
8
About
Neha Baranwal’s research bridges human-robot interaction (HRI), assistive technology, and multimodal communication, with a focus on making robots more accessible and intuitive. Her major contributions lie in developing systems that recognize Indian Sign Language (ISL) gestures and Hindi speech, enabling seamless communication between hearing-impaired communities and machines. Her 2015 paper on continuous dynamic ISL gesture recognition (46 citations) established foundational techniques for real-time gesture interpretation, while her work on MFCC-based Hindi speech recognition (29 citations) advanced voice-controlled robotics. Baranwal innovatively applied possibility theory to handle ambiguity in gesture recognition, as seen in her 2015 and 2017 papers (14 and 17 citations), and explored multimodal fusion of gesture and speech for higher accuracy (6 citations). Beyond recognition, she investigated humanoid robots for sketch drawing and collaborative robot teams that provide verbal explanations of their actions (13 citations). Her research also extends to socially impactful applications, such as a human-robot interaction framework for criminal identification based on eyewitness perception. With over 175 total citations, Baranwal’s work stands out for its integration of Indian languages and sign languages into robotics, making HRI more inclusive and culturally relevant.
Research Focus
Key Achievements
Top Papers
- 1
- 2A MFCC based Hindi speech recognition technique using HTK Toolkit29 citations · 2013
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- 5Possibility theory based continuous Indian Sign Language gesture recognition14 citations · 2015
- 6Verbal explanations by collaborating robot teams13 citations · 2020
- 7Development of a self reliant humanoid robot for sketch drawing12 citations · 2017
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