Title :
Speech synthesis using two-sided linear prediction parameters
Author :
Leung, S.H. ; Ng, H.C. ; Wong, K.F.
Author_Institution :
Dept. of Electron. Eng., City Polytech. of Hong Kong, Kowloon, Hong Kong
Abstract :
A two-sided linear prediction (TSLP) model is shown to have high prediction gain over the conventional linear prediction (LPC) model [David and Ramamurthi, 1991], while it requires fewer coefficients in modeling. Unfortunately, speech synthesis cannot use the TSLP model directly because it needs future samples which are not available in the process. Autoregressive spectral matching (ARSM) is proposed to render the TSLP model suitable for speech synthesis. Vector sum excitation method is used to generate the excitation to the new model and its performance is comparable to the standard VSELP
Keywords :
analogue-digital conversion; linear predictive coding; speech coding; speech synthesis; stochastic processes; time series; ARSM; autoregressive spectral matching; speech synthesis; standard VSELP; two-sided linear prediction parameters; vector sum excitation method; Autocorrelation; Cities and towns; Equations; Image processing; Linear predictive coding; Neural networks; Power system modeling; Predictive models; Speech processing; Speech synthesis;
Conference_Titel :
Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on
Print_ISBN :
0-7803-1865-X
DOI :
10.1109/SIPNN.1994.344843
