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KMID : 1022420160080030031
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Phonetics and Speech Sciences
2016 Volume.8 No. 3 p.31 ~ p.38
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Development of articulatory estimation model using deep neural network
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You Hee-Jo
Yang Hyung-Won
Kang Jae-Koo
Cho Young-Sun
Hwang Sung-Hah
Hong Yeon-Jung
Cho Ye-Jin
Kim Seo-Hyun
Nam Ho-Sung
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Abstract
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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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KEYWORD
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the Wisconsin X-ray Microbeam Database, speech inversion, artificial neural network, deep neural network
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