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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Araújo, G.F., Macedo, H.T. (2012). Context-Sensitive ASR for Controlling the Navigation of Mobile Robots. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds) Advances in Artificial Intelligence - SBIA 2012. SBIA 2012. Lecture Notes in Computer Science(), vol 7589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34459-6_20
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DOI: https://doi.org/10.1007/978-3-642-34459-6_20
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