Optimal transformation of LSP parameters using neural network

In this paper, the intraframe correlation properties of Line Spectrum Pair (LSP) are used to develop an efficient encoding algorithm using the Karhunen-Loeve (KL) transformation. An important nonuniform statistical characteristics of LSP frequencies are investigated. Based upon this nonuniform property the neural network based techniques for generating the transform vectors via system training are studied. Using Principal Component Analysis (PCA) network to decorrelate LSP coefficients, we show that these new approaches lead to as good or better distortion as compared to other methods for speech analysis-synthesis