Excitation Modeling Based on Waveform Interpolation for HMM-based Speech Synthesis

It is generally known that a well-designed excitation produces high quality signals in hidden Markov model (HMM)-based speech synthesis systems. This paper proposes a novel techniques for generating excitation based on the waveform interpolation (WI). For modeling WI parameters, we implemented statistical method like principal component analysis (PCA). The parameters of the proposed excitation modeling techniques can be easily combined with the conventional speech synthesis system under the HMM framework. From a number of experiments, the proposed method has been found to generate more naturally sounding speech.