Development of articulatory estimation model using deep neural network

심층신경망을 이용한 조음 예측 모형 개발

  • 유희조 (고려대학교 심리학과) ;
  • 양형원 (고려대학교 영어영문학과) ;
  • 강재구 (고려대학교 영어영문학과) ;
  • 조영선 (고려대학교 영어영문학과) ;
  • 황성하 (고려대학교 영어영문학과) ;
  • 홍연정 (고려대학교 영어영문학과) ;
  • 조예진 (고려대학교 영어영문학과) ;
  • 김서현 (고려대학교 영어영문학과) ;
  • 남호성 (고려대학교)
  • Received : 2016.05.30
  • Accepted : 2016.09.20
  • Published : 2016.09.30


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.



Supported by : 한국연구재단


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