Robust Spoken Language Understanding for House Service Robots

Abstract

Service robotics has been growing significantly in the last years, leading to several research results and to a number of consumer products. One of the essential features of these robotic platforms is represented by the ability of interacting with users through natural language. Spoken commands can be processed by a Spoken Language Understanding chain, in order to obtain the desired behavior of the robot. The entry point of such a process is represented by an Automatic Speech Recognition (ASR) module, that provides a list of transcriptions for a given spoken utterance. Although several well-performing ASR engines are available off-the-shelf, they operate in a general purpose setting. Hence, they may be not well suited in the recognition of utterances given to robots in specific domains. In this work, we propose a practical yet robust strategy to re-rank lists of transcriptions. This approach improves the quality of ASR systems in situated scenarios, i.e., the transcription of robotic commands. The proposed method relies upon evidences derived by a semantic grammar with semantic actions, designed to model typical commands expressed in scenarios that are specific to human service robotics. The outcomes obtained through an experimental evaluation show that the approach is able to effectively outperform the ASR baseline, obtained by selecting the first transcription suggested by the ASR.