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http://dx.doi.org/10.7471/ikeee.2019.23.4.1440

Motion Study of Treatment Robot for Autistic Children Using Speech Data Classification Based on Artificial Neural Network  

Lee, Jin-Gyu (Dept. of Electrical Engineering, Semyung University)
Lee, Bo-Hee (Dept. of Electrical Engineering, Semyung University)
Publication Information
Journal of IKEEE / v.23, no.4, 2019 , pp. 1440-1447 More about this Journal
Abstract
Currently, the prevalence of autism spectrum disorders in children is reported to be higher and shows various types of disorders. In particular, they are having difficulty in communication due to communication impairment in the area of social communication and need to be improved through training. Thus, this study proposes a method of acquiring voice information through a microphone mounted on a robot designed through preliminary research and using this information to make intelligent motions. An ANN(Artificial Neural Network) was used to classify the speech data into robot motions, and we tried to improve the accuracy by combining the Recurrent Neural Network based on Convolutional Neural Network. The preprocessing of input speech data was analyzed using MFCC(Mel-Frequency Cepstral Coefficient), and the motion of the robot was estimated using various data normalization and neural network optimization techniques. In addition, the designed ANN showed a high accuracy by conducting an experiment comparing the accuracy with the existing architecture and the method of human intervention. In order to design robot motions with higher accuracy in the future and to apply them in the treatment and education environment of children with autism.
Keywords
Autism Spectrum Disorders; Autistic Children Training; MFCC; Convolutional Neural Network; Speech Data Classification;
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