In their verbal interactions, humans are often afforded with language barriers and communication problems and disabilities. This problem is even more serious in the fields of education and health care for children with special needs. The use of robotic agents, notably humanoids integrated within human groups, is a very important option to face these limitations. Many scientific research projects attempt to provide solutions to these communication problems by integrating intelligent robotic agents with natural language communication abilities. These agents will thus be able to help children suffering from verbal communication disorders, more particularly in the fields of education and medicine. In addition, the introduction of robotic agents into the child’s environment creates stimulating effects for more verbal interaction. Such stimulation may improve their ability to interact with pairs. In this paper, we propose a new approach for the human-robot multilingual verbal interaction based on hybridization of recent and performant approach on translation machine system consisting of neural network model reinforced by a large distributed domain-ontology knowledge database. We have constructed this ontology by crawling a large number of educational web sites providing multi-lingual parallel texts and speeches. Furthermore, we present the design of augmented LSTM neural Network models and their implementation to permit, in learning context, communication between robots and children using multiple natural languages. The model of a general ontology for multilingual verbal communication is produced to describe a set of linguistic and semantic entities, their properties and relationships. This model is used as an ontological knowledge base representing the verbal communication of robots with children.
M. Qbadou, I. Salhi, H. El Fazazi, K. Mansouri, M. Manios, V. Kaburlasos, “Human-robot multilingual verbal communication – the ontological knowledge and learning-based models”, Advances in Science, Technology and Engineering Systems (ASTES) Journal, vol. 5, no. 4, pp. 540-547, 2020.