Sbia2012

Context-sensitive ASR for controlling the navigation of mobile robots
Gabriel Ferreira Araujo and Hendrik Teixeira Macedo
Computer Science Department, Federal University of Sergipe, S˜o Cristov˜o SE a a 49100000, Brazil, [e-mail], [e-mail]

Abstract. Automatic Speech Recognition (ASR) is a complex task, which depends on language, vocabulary and context. In the navigation control of mobile robots, the set of possible interpretations for a command utterance may be reduced in favor of the recognition rate increase, if we consider that the robot’s work environment is quite defined and with constant elements. In this paper we propose a contextual model in addition to the acoustic and language models used by mainstream ASRs. We provide a whole mobile robot navigation system which use contextual information to improve the recognition rate of speech-based commands. Recognition accuracy has been evaluated by Word Information Lost (WIL) metric. Results show that the insertion of a contextual model provides a improvement around 3% on WIL.

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Introduction

Human-Robot Interaction (HRI) is a important multidisciplinary research field, which aims to understand, design, and evaluate every communication form among humans and robots [5]. HRI applications where humans and robots are placed within the same room, are usually classified as proximate interaction, which seems to favor the use of gesture-based or speech-based human-robot interaction kind [2]. Even though multimodal interfaces have been recently proposed in order to provide more intuitive human-robot proximate interaction [6], we are interested in speech-based interface issues, due the advantage very well posed by [12]: “according to the diffraction property of audio signal, the sound can bypass obstacles” and so, a speech-based system may suit better for a mobile robot navigation control, as has been usually applied [13], [1]. In order to be effective, though, speech-based interfaces need high