Spectral Envelope Linear Predictive Analysis of Speech based on the Spectral Autocorrelation
Hong-Kook Kim, Hwang-Soo Lee · International Symposium on Information Theory and its Applications · 1994
In this paper, we propose a new linear predictive analysis method where the autocorrelation of speech signal is estimated from the spectral envelope of the speech signal on the basis of the spectral autocorrelation. The spectral autocorrelation is defined as the autocorrelation of discrete quantities of speech spectrum with spectral resolution identical to the discrete Fourier transform (DFT) used to obtain the speech spectrum. The characteristic of the spectral autocorrelation for voiced and unvoiced speech is derived analytically and imperially, respectively. The spectral envelope is obtained by smoothing the fine structure of a speech spectrum using the fundamental frequency estimated by the spectral autocorrelation of speech spectrum. The conventional linear prediction analysis is applied to the sample autocorrelation sequences obtained from the inverse DFT of the spectral envelope. From the comparison to LP we can observe that SELP provides lower order representation of speech than LP.