Phoneme-to-Gesture Translation in Human-Robot Interaction: Merging Computational Linguistics with Mechanical Actuation

Abstract

Human-Robot Interaction (HRI) is at the forefront of modern technological research, especially in the context of improving natural and intuitive communication between humans and machines. One emerging area is the translation of spoken language into mechanical gestures in humanoid robots. This paper reviews the interdisciplinary methods and challenges associated with phoneme-to-gesture translation. Integrating computational linguistics, speech recognition, NLP, and mechanical actuation, this study delves into how phonemes—the smallest units of speech—can be mapped to physical gestures in real-time.

Recent advances in speech recognition, such as wav2vec 2.0 and DeepSpeech, have improved phoneme parsing accuracy. This facilitates more precise mapping algorithms that utilize both rule-based and deep learning models. The complexity lies not only in interpreting speech but also in coordinating robotic actuators via inverse kinematics and sensor feedback. The paper also investigates the role of reinforcement learning in refining gesture execution through adaptive feedback loops.

Ethical and cultural implications are discussed to ensure the gestures are inclusive and appropriate. Future research avenues such as multimodal integration, real-time constraints, and cross-platform deployment are explored. This review provides a comprehensive outlook on the evolution and potential of phoneme-to-gesture translation, setting the foundation for more expressive and socially intelligent robots.