A deterministic plus noise model of excitation signal using principal component analysis for parametric speech synthesis

This paper proposes a new approach of modeling the excitation signal as deterministic and noise components. Initially, a study on characteristics of excitation or residual signal around glottal closure instant (GCI) is performed using principal component analysis (PCA). Based on the study, the segment of residual signal around GCI is considered as the deterministic component and the remaining part of the residual signal is considered as the noise component. The deterministic component is parameterized using PCA coefficients, and the noise component can be represented in terms of spectral and amplitude envelopes. The proposed excitation modeling approach is incorporated in the HMM-based speech synthesis system. Subjective evaluation results show a significant improvement in the quality of speech synthesized by the proposed method, compared to three existing methods.