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심층신경망을 이용한 조음 예측 모형 개발Development of articulatory estimation model using deep neural network

Other Titles
Development of articulatory estimation model using deep neural network
Authors
유희조양형원강재구조영선황성하홍연정조예진김서현남호성
Issue Date
2016
Publisher
한국음성학회
Keywords
the Wisconsin X-ray Microbeam Database; speech inversion; artificial neural network; deep neural network
Citation
말소리와 음성과학, v.8, no.3, pp.31 - 38
Indexed
KCI
Journal Title
말소리와 음성과학
Volume
8
Number
3
Start Page
31
End Page
38
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/90889
DOI
10.13064/KSSS.2016.8.3.031
ISSN
2005-8063
Abstract
Speech inversion (acoustic-to-articulatory mapping) is not a trivial problem, despite the importance, due to the highly non-linear and non-unique nature. This study aimed to investigate the performance of Deep Neural Network (DNN) compared to that of traditional Artificial Neural Network (ANN) to address the problem. The Wisconsin X-ray Microbeam Database was employed and the acoustic signal and articulatory pellet information were the input and output in the models. Results showed that the performance of ANN deteriorated as the number of hidden layers increased. In contrast, DNN showed lower and more stable RMS even up to 10 deep hidden layers, suggesting that DNN is capable of learning acoustic-articulatory inversion mapping more efficiently than ANN.
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문과대학 (영어영문학과)
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