Integrating Persian Lip Reading in Surena-V Humanoid Robot for Human-Robot Interaction
This work addresses the challenge of enhancing human-robot interaction in noisy or crowded environments, such as caregiving and customer service, though it is incremental as it applies existing methods to a new language and robot platform.
This study tackled the problem of improving speech recognition for humanoid robots in social settings by integrating Persian lip-reading technology into the Surena-V robot, achieving 89% accuracy with an LSTM model for real-time human-robot interaction.
Lip reading is vital for robots in social settings, improving their ability to understand human communication. This skill allows them to communicate more easily in crowded environments, especially in caregiving and customer service roles. Generating a Persian Lip-reading dataset, this study integrates Persian lip-reading technology into the Surena-V humanoid robot to improve its speech recognition capabilities. Two complementary methods are explored, an indirect method using facial landmark tracking and a direct method leveraging convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The indirect method focuses on tracking key facial landmarks, especially around the lips, to infer movements, while the direct method processes raw video data for action and speech recognition. The best-performing model, LSTM, achieved 89\% accuracy and has been successfully implemented into the Surena-V robot for real-time human-robot interaction. The study highlights the effectiveness of these methods, particularly in environments where verbal communication is limited.