Statistical F0 prediction for electrolaryngeal speech enhancement considering generative process of F0 contours within product of experts framework
Kou Tanaka, Hirokazu Kameoka, Tomoki Toda, Satoshi Nakamura · 2016
We have previously proposed a statistical fundamental frequency (F0) prediction method that makes it possible to predict the underlying F0contour of electrolaryngeal (EL) speech from its spectral feature sequence. Although this method was shown to contribute to improving the naturalness of EL speech as a whole, the predicted F0contour was still unnatural compared with that in normal speech. One possible solution to improve the naturalness of the predicted F0contours would be to take account of the physical mechanism of vocal phonation. Recently a statistical model of voice F0contours was formulated by constructing a stochastic counterpart of the Fujisaki model, a well-founded mathematical model representing the control mechanism of vocal fold vibration. This paper proposes a Product-of-Experts model to incorporate this generative model of voice F0contours into the statistical F0prediction model. Based on the constructed model, we derive algorithms for parameter training and F0prediction. Experimental results revealed that the proposed method successfully outperformed our previously proposed method in terms of the naturalness of the predicted F0contours.