Modeling unvoiced sounds in statistical parametric speech synthesis with a continuous vocoder
Tamás Gábor Csapó, Géza Németh, Miloš Cerňak, Philip N. Garner · 2016
In this paper, we introduce an improved excitation model for statistical parametric speech synthesis. Our earlier vocoder [1], which applies continuous F0 in combination with Maximum Voiced Frequency (MVF), is extended. The focus of this paper is on the modeling of unvoiced consonants, for which two alternative methods are proposed. The first method applies no postprocessing during MVF estimation to reduce the unwanted voiced component of unvoiced speech sounds. The second separates voiced and unvoiced excitation based on the phonetic labels of the text to be synthesized. In an objective experiment we found that the first method produces unvoiced sounds that are closer to natural speech in terms of Harmonics-to-Noise Ratio. A subjective listening test showed that both methods are more natural than our baseline system, and the second method is significantly preferred.