Deep learning speech recognition for residential assistant robot

Jiménez-Moreno R.; Castillo R.A.

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Deep learning speech recognition for residential assistant robot

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摘要

This work presents the design and validation of a voice assistant to command robotic tasks in a residential environment, as a support for people who require isolation or support due to body motor problems. The preprocessing of a database of 3600 audios of 8 different categories of words like “paper”, “glass” or “robot”, that allow to conform commands such as "carry paper" or "bring medicine", obtaining a matrix array of Mel frequencies and its derivatives, as inputs to a convolutional neural network that presents an accuracy of 96.9 in the discrimination of the categories. The command recognition tests involve recognizing groups of three words starting with "robot", for example, "robot bring glass", and allow identifying 8 different actions per voice command, with an accuracy of 88.75. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

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《IAES International Journal of Artificial Intelligence》 |2023年第2期|585-592|共8页
作者
Jiménez-Moreno R.; Castillo R.A.;
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作者单位

Department of Mechatronic Engineering, Militar Nueva Granada University;

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原文格式 PDF
正文语种英语
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关键词
Assistant robot; Ceptral coefficients; Convolutional network; Speech recognition; Voice command;

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Deep learning speech recognition for residential assistant robot

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