-
The given English phonemes, words and sentences are sampled and preprocessed. For these real measured speech signal series, time delay and embedding dimension are determined by using mutual information method and Cao's method, respectively, so as to perform phase space reconstruction of the speech signal series. By using small data set method, the largest Lyapunov exponent of the speech signal series is calculated and the fact that its value is greater than zero presents chaotic characteristics of the speech signal series. This, in fact, performs the chaotic characteristic identification of the speech signal series. By introducing second-order Volterra series, in this paper we put forward a type of nonlinear prediction model with an explicit structure. To overcome some intrinsic shortcomings caused by improper parameter selection when using the least mean square (LMS) algorithm to update Volterra model efficiency, by using a variable convergence factor technology based on a posteriori error assumption on the basis of LMS algorithm, a novel Davidon-Fletcher-Powell-based second of Volterra filter (DFPSOVF) is constructed and is performed to predict speech signal series of the given English phonemes, words and sentences with chaotic characteristics. Simulation results under MATLAB 7.0 environment show that the proposed nonlinear model DFPSOVF can guarantee its stability and convergence and there are no divergence problems in using LMS algorithm; for single-frame and multi-frame of the measured speech signals, when root mean square error (RMSE) is used as an evaluation criterion the prediction accuracy of the proposed nonlinear prediction model DFPSOVF in this paper is better than that of the linear prediction (LP) that is traditionally employed. The primary results of single-frame and multi-frame predictions are given. So, the proposed DFPSOVF model can substitute linear prediction model on certain conditions. Meanwhile, it can better reflect trends and regularity of the speech signal series and fully meet requirements for speech signal prediction. The memory length of the proposed prediction model may be selected by the embedding dimension of the speech signal series. The proposed model can present a nonlinear analysis and more valuable model structure for speech signal series, and opens up a new way to speech signal reconstruction and compression coding so as to improve complexity and process effect of speech signal processing method.
-
Keywords:
- speech signal /
- chaos /
- Volterra prediction model /
- Davidon-Fletcher-Powell algorithm
[1] Maragos P 2013 Appl. Soft Comput. 13 3314
[2] Wu X J, Yang Z Z 2013 Appl. Soft Comput. 13 3314
[3] Max A L 2011 Advances in Nonlinear Speech Processing 7015 9
[4] Cheng X F, Zhang Z 2013 Acta Phys. Sin. 62 168701 (in Chinese) [成谢锋, 张正 2013 62 168701]
[5] Chen D Y, Liu Y, Ma X Y 2012 Acta Phys. Sin. 61 100501 (in Chinese) [陈帝伊, 柳烨, 马孝义 2012 61 100501]
[6] Iasonas K, Petros M 2005 IEEE Trans. Speech Audio Process. 13 1098
[7] Sun J F, Zheng N H, Wang X L 2007 Singal Process. 87 2431
[8] Maciej O 2005 Chin. Phys. 14 2181
[9] Xiao X C, Li H C, Zhang J S 2005 Chin. Phys. 14 2181
[10] Zhang J S, Li H C, Xiao X C 2005 Chin. Phys. 14 49
[11] Thyssen J, Nielsen H, Hansen S D 1994 ICASSP 185
[12] Sigrist Z, Grivel E, Alcoverro B 2012 Signal Process. 92 1010
[13] Mathews V J 1991 IEEE Signal Process. Mag. 8 10
[14] Wei R X, Han C Z, Zhang Z L 2005 Acta Electron. Sin. 33 656 (in Chinese) [魏瑞轩, 韩崇昭, 张宗麟 2005电子学报 33 656]
[15] Guerin A, Faucon G, Le Bouquin-Jeannes R 2003 IEEE Trans. Speech Audio Proc. 11 672
[16] Zhang Y M, Wu X J, Bai S L 2013 Acta Phys. Sin. 62 190509 (in Chinese) [张玉梅, 吴晓军, 白树林 2013 62 190509]
[17] Henry D, Abarbanel N M, Rabinovich M I, Evren T 2001 Phys. Lett. A 281 368
[18] Cao L Y 1997 Physica D 110 43
[19] Rosenstein M T, Collins J J, de Iuca C J 1993 Physica D 65 117
[20] de Campos M L R, Antoniou A 1997 IEEE Trans. Circ. Syst. 44 924
-
[1] Maragos P 2013 Appl. Soft Comput. 13 3314
[2] Wu X J, Yang Z Z 2013 Appl. Soft Comput. 13 3314
[3] Max A L 2011 Advances in Nonlinear Speech Processing 7015 9
[4] Cheng X F, Zhang Z 2013 Acta Phys. Sin. 62 168701 (in Chinese) [成谢锋, 张正 2013 62 168701]
[5] Chen D Y, Liu Y, Ma X Y 2012 Acta Phys. Sin. 61 100501 (in Chinese) [陈帝伊, 柳烨, 马孝义 2012 61 100501]
[6] Iasonas K, Petros M 2005 IEEE Trans. Speech Audio Process. 13 1098
[7] Sun J F, Zheng N H, Wang X L 2007 Singal Process. 87 2431
[8] Maciej O 2005 Chin. Phys. 14 2181
[9] Xiao X C, Li H C, Zhang J S 2005 Chin. Phys. 14 2181
[10] Zhang J S, Li H C, Xiao X C 2005 Chin. Phys. 14 49
[11] Thyssen J, Nielsen H, Hansen S D 1994 ICASSP 185
[12] Sigrist Z, Grivel E, Alcoverro B 2012 Signal Process. 92 1010
[13] Mathews V J 1991 IEEE Signal Process. Mag. 8 10
[14] Wei R X, Han C Z, Zhang Z L 2005 Acta Electron. Sin. 33 656 (in Chinese) [魏瑞轩, 韩崇昭, 张宗麟 2005电子学报 33 656]
[15] Guerin A, Faucon G, Le Bouquin-Jeannes R 2003 IEEE Trans. Speech Audio Proc. 11 672
[16] Zhang Y M, Wu X J, Bai S L 2013 Acta Phys. Sin. 62 190509 (in Chinese) [张玉梅, 吴晓军, 白树林 2013 62 190509]
[17] Henry D, Abarbanel N M, Rabinovich M I, Evren T 2001 Phys. Lett. A 281 368
[18] Cao L Y 1997 Physica D 110 43
[19] Rosenstein M T, Collins J J, de Iuca C J 1993 Physica D 65 117
[20] de Campos M L R, Antoniou A 1997 IEEE Trans. Circ. Syst. 44 924
Catalog
Metrics
- Abstract views: 6426
- PDF Downloads: 232
- Cited By: 0
下载:
