(Research On Technology Of Chinese Speech Coding Based On Nonlinear Theory)

Speech digital analysis and treatment are important process of speech digital transmission and digital storage. With the development of speech communication technology, advantages of high quality and low bandwidth and so on have been pursuing by people. Speech coding plays a significant role in the process of achieving the goal.At present, the analysis and prediction of speech signal are all using linear theory and linear prediction technique, but the speech production system is complicated nonlinear and has chaotic property as well as fractal feature, so linear methods can’t fundamentally improve performance of the speech transmission and storage. Therefore, the nonlinear characteristic of Chinese speech are further studied, combined with Radical Basis Function Network(RBF Network for short), a nonlinear predictor is designed. Then a nonlinear predictive code system is designed based on the predictor. Main works and results are as follows:(1) Speech signal chaotic property detection and fractal featureBased on nonlinear theory, nonlinear characteristic parameters of Chinese speech phonemes are studied. The maximum Lyapunov components of33Chinese speech phonemes are solved by Wolf-algorithm. The results indicate Chinese speech has chaotic characteristics. Correlation dimensions of33Chinese speech phonemes are solved by GP-algorithm, the results show that the production system of voiced are low-dimensional system, and the production system of some unvoiced are high-dimensional system.(2) Phase space reconstruction of speech signalTheoretical basis of speech signal nonlinear prediction and prediction tools are analyzed, and methods of solving phase space reconstruction parameters containing delay time、embed dimension are further studied, which are firstly solved by C-C algorithm, according to the limitation of results, then combined with auto-correlation algorithm and FNN(False Neatest Neighbors) algorithm are solving respectively. According to select sample rate and speech source at experimentations, statistical method is used to study. The results show that sample rate and speech source have little influence on delay time and embed dimension.They have strong robustness.(3) Nonlinear predictor model based on RBF networkCombined with nonlinear characteristics of Chinese speech signal and Radical Basis Function (RBF) network analysis methods, The averages of the delay time and embedding dimension for33Chinese speech phonemes determine the neurons number of the three layers for RBF neural network model, nonlinear prediction model based on RBF network is designed. Compared with the ADPCM linear predictor, the simulation results indicate prediction error of nonlinear predictor based on RBF network is significantly decreased and has higher performance as well as prediction accuracy.(4) Speech enhanced treatment based on wavelet transformPredictive coding performance of speech signal may drop swiftly at noise circumstance, to be aimed at this problem, speech enhanced treatment technologies based on wavelet transform are studied. Designing threshold function in wavelet threshold de-noising algorithm is studied primarily. On one hand, in order to overcome the drawback of the traditional threshold selection difficult to adapt to the non-stationary noise in threshold denoising algorithm, this paper get noise estimated with real-time changes by applied the MCRA algorithm to the wavelet domain to calculate the noise variance and get adaptive adjustment threshold value by used of spectral flatness. On the other hand, An improved threshold function design on the basis of non-negative dead zone threshold function which not only has good continuity but also overcome the lack of the fixed deviation existence in the soft threshold function and considers the characteristics of the attenuation of the noise wavelet modulus values conform exponentially.(5) The design of speech E-CENP code systemBased on the nonlinear prediction model, CELP speech coding algorithm and enhanced treatment are applied to design a nonlinear predictive coding system——E-CENP whose pretreatment joined enhanced treatment. Linear predictor of CELP is replaced with the nonlinear prediction model. The simulation results indicate:Compared with the linear predictive coding system, nonlinear predictive coding system has high quality、good robustness and so on.Based on theories of nonlinear dynamics, a nonlinear predictive coding system——E-CENP is designed. Compared with CELP coding system, the acoustics of the speech signal after by decoding is higher and has good robustness. The results show that the new methods and theories of nonlinear dynamics are adapt to speech,which provides a new idea and solution to the research of technique of speech processing.