Spoken language understanding for social robotics

Abstract

Speech understanding is a fundamental feature of social robots, since spoken language is the most natural mean of human-human communication. Providing a robot with the ability to understand human language makes it much more accessible to a wide range of users, especially for those who are not experts in the field. Speech understanding is composed of two sub-tasks. The first one is known as automatic speech recognition (ASR), which is the process of translating or transcribing an audio signal into a written text. The second one is natural language understanding (NLU), which consists in obtaining a semantic interpretation from the (previously) transcribed text. In this work, we present a speech-input natural language understanding system for social robots which has been successfully tested with the well-known HuRIC v1.2 corpus obtaining state-of-the art results. Preliminary versions of the proposed system were also tested in real scenarios during the last two editions of the RoCKIn@Home competition, where we were classified in first and second positions respectively.