Speech synthesis using two-sided linear prediction parameters

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.<
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