Evaluation of detailed modeling of the LP residual in statistical speech synthesis

Abstract

Speech parameterization remains an open question in statistical speech synthesis. In our earlier work we have shown that a framework developed originally for highly efficient speech storage can also be successfully applied for voice conversion and concatenative unit selection based speech synthesis. Recently, we have also used the same coding scheme in hybrid-form speech synthesis. In this paper, we further discuss the framework and apply it in statistical speech synthesis, concentrating specifically on the spectral modeling of the linear prediction (LP) residual. Perceptual evaluation demonstrates that the modeling of the spectral details remaining in the residual improves the quality of synthetic speech.