On the implementation of the harmonic plus noise model for concatenative speech synthesis

In concatenative speech synthesis systems, speech models are usually used to represent the speech signal. Recently, the Harmonic plus Noise Model, HNM, has been proposed for concatenative speech synthesis with promising results. One main drawback of HNM is its complexity. In this paper, we review four different methods of reducing the complexity of HNM. These include, straight-forward synthesis, synthesis using inverse Fast Fourier Transform, synthesis using Recurrence Relations for trigonometric functions, RR, and synthesis based on Delayed Multi-Resampled Cosine functions, DMRC. DMRC was shown to outperform all the other techniques reducing the complexity of HNM synthesizer by 95% compared to the current version of the HNM which is based on the SF method. Informal listening tests showed that the version of HNM based on the DMRC method provides higher quality of speech synthesis than the version based on SF.