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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
- 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
- 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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Related Researcher
- Nam, Ho sung
- College of Liberal Arts (Department of English Language and Literature)