Open AccessHidden Phase Space Reconstruction: A Novel Chaotic Time Series Prediction Method for Speech Signals
Author(s) -
Li Jingjing,
Zhang Qijin,
Zhang Yumei,
Wu Xiaojun,
Wang Xiaoming,
Su Yuping
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.09.010
Subject(s) - chaotic , embedding , computer science , algorithm , phase space , nonlinear system , dimension (graph theory) , artificial neural network , series (stratigraphy) , signal (programming language) , speech coding , signal reconstruction , frame (networking) , time series , speech recognition , mathematics , artificial intelligence , signal processing , machine learning , paleontology , telecommunications , physics , quantum mechanics , biology , pure mathematics , thermodynamics , programming language , radar
Speech signals are nonlinear chaotic time series. This paper proposes a novel speech signal nonlinear prediction model with the hidden phase space reconstruction method. The parameters, embedding dimension m , time delay τ and model structure are solved simultaneously, breaking the restriction of phase space, which needs to be reconstructed before modeling for the existing prediction method. Subsequently, an explicit speech signal prediction model is generated. Meanwhile, the introduction of the frame length parameter k effiectively extends the prediction length. Experimental results show that the values of m and τ solved by the proposed method are consistent with the values addressed by the Cao method and mutual information method, respectively. In addition, the optimal value of k is further discussed. The prediction results obtained using the proposed model are more accurate than those of linear prediction coding, the radial basis function neural network model and the long short‐term memory network.