A Continuous Vocoder Using Sinusoidal Model for Statistical Parametric Speech Synthesis

Abstract

In our earlier work in statistical parametric speech synthesis, we proposed a source-filter based vocoder using continuous F0 (contF0) in combination with Maximum Voiced Frequency (MVF), which was successfully used with deep learning. The advantage of a continuous vocoder in this scenario is that vocoder parameters are simpler to model than conventional vocoders with discontinuous F0. However, our vocoder lacks some degree of naturalness and still not achieving a high-quality speech synthesis compared to the well-known vocoders (e.g. STRAIGHT or WORLD). Previous studies have shown that human voice can be modelled effectively as a sum of sinusoids. In this paper, we firstly address the design of a continuous vocoder using sinusoidal synthesis model that is applicable in statistical frameworks. The same three parameters of the analysis part from our previous model have been also extracted and used for this study. For refining the output of the contF0 estimation, post-processing approach is utilized to reduce the unwanted voiced component of unvoiced speech sounds, resulting in a smoother contF0 track. During synthesis, a sinusoidal model with minimum phase is applied to reconstruct speech. Finally, we have compared the voice quality of the proposed system to the STRAIGHT and WORLD vocoders. Experimental results from objective and subjective evaluations have shown that the proposed vocoder gives state-of-the-art vocoders performance in synthesized speech while outperforming the previous work of our continuous F0 based source-filter vocoder.