Automatic speech recognition system for controlling a robotic system using Romanian

In this paper, we describe a proposed Automatic Speech Recognition system that can be used to give vocal commands to a robotic system. The system was specially created to use Romanian as the main language with respect to its particularities: specific phonetic rules and large variety of accents. The...

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Published in:Оптико-електронні інформаційно-енергетичні технології
Date:2008
Main Authors: Goloca, A., Pentiuc, S.G.
Format: Article
Language:English
Published: Інститут фізики напівпровідників імені В.Є. Лашкарьова НАН України 2008
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Online Access:https://nasplib.isofts.kiev.ua/handle/123456789/32189
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Journal Title:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Cite this:Automatic speech recognition system for controlling a robotic system using Romanian / A. Goloca, S.G. Pentiuc // Оптико-електронні інформаційно-енергетичні технології. — 2008. — № 2 (16). — С. 109-113. — Бібліогр.: 12 назв. — англ.

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Digital Library of Periodicals of National Academy of Sciences of Ukraine
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Summary:In this paper, we describe a proposed Automatic Speech Recognition system that can be used to give vocal commands to a robotic system. The system was specially created to use Romanian as the main language with respect to its particularities: specific phonetic rules and large variety of accents. The main purpose of the system is to act as a touch-less interface for a demonstrative robotic system. This interface was designed to allow a remote human user to give simple commands to the robotic system without any physical contact with the robot. There are more ways to create an Automatic Speech Recognition System but, given the complexity of the task, the methods that were used had to be reliable and this is the reason while the Hidden Markov Models approach was chosen. The current status of the project allows a remote user to give simple commands to the experimental robot using a microphone, a laptop or a PDA and wireless connection.
ISSN:1681-7893