Voice-enabled human-robot interaction: adaptive self-learning systems for enhanced collaboration

Abstract This research proposes an adaptive human-robot interaction (HRI) that combines voice recognition, emotional context detection, decision-making, and self-learning. The aim is to overcome challenges in dynamic and noisy environments while achieving real-time and scalable performance. The architecture is based on a three-stage HRI system: voice input acquisition, feature extraction, and adaptive decision-making. For voice recognition, modern pre-processing techniques and mel-frequency cepstral coefficients are used to robustly implement the commands. Emotional context detection is governed by neural network classification on pitch, energy, and jitter features. Decision-making uses reinforcement learning where actions are taken and then the user is prompted to provide feedback that serves as a basis for re-evaluation. Iterative self-learning mechanisms are included, thereby increasing the adaptability as stored patterns and policies are updated dynamically. The experimental results show substantial improvements in recognition accuracy along with task success rates and emotional detection. The proposed system achieved 95% accuracy and a task success rate of 96%, even against challenging noise conditions. It is apparent that emotional detection achieves a high F1-score of 92%. Real-world validation showed the system’s ability to dynamically adapt, thus mitigating 15% latency through self-learning. The proposed system has potential applications in assistive robotics, interactive learning systems, and smart environments, addressing scalability and adaptability for real-world deployment. Novel contributions to adaptive HRI arise from the integration of voice recognition, emotional context detection, and self-learning mechanisms. The findings act as a bridge between the theoretical advancements and the practical utility of further system improvements in human-robot collaboration.