Voice-Interactive Learning Dialogue on a Low-Cost Device

Martin Bulín, Martin Adamec, Petr Neduchal, Marek Hrúz, Jan Svec

Abstract: Traditional offline learning approaches are reaching their limits in meeting the dynamic demands of specialized applications, such as real-time human-robot interaction. While high benchmark scores attained through offline fine-tuning large models on extensive data, offer a glimpse of their potential, the true functionality is validated when these models are deployed on target devices and utilized in real-life scenarios. This paper presents a method incorporating humans in an interactive learning loop, using their real-time feedback for online neural network retraining. By leveraging the power of transfer learning, we can proficiently adapt the model to suit the specific requirements of the target application through natural voice-based dialogue. The approach is evaluated on the image classification task utilizing a unique low-cost device and a practical example of the real-time dialogue is presented to demonstrate the functionality.

External IDs:dblp:conf/acpr/BulinANHS23