Speech Understanding System for Emotional Companion Robots

Abstract

Within the project of emotional robot F-2 we develop a natural text parser for automatic speech comprehension. It is aimed at the construction of semantic representation and the selection of “scripts” – units for inference modelling and the selection of emotional reactions for the robot. The design of the speech understanding system follows the traditional concept of linguistic levels: it consequently constructs morphological, syntactic and semantic representations. Unlike neural networks, these representations are readable for a developer, so the accumulated data is available for statistical and analytical processing. Parser accepts written text after speech recognition (during actual talks with the robot) or during processing of everyday news and blogs from the internet. Parser may save text representations on each level of linguistic model to a database. In particular, semantic representations from daily internet processing are used as an accumulated knowledge database for the robot. In this paper we discuss the approach to the model of understanding through a competition of scripts on a robot companion.