STATISTICAL F0 PREDICTION FOR ELECTROLARYNGEAL SPEECH ENHANCEMENT CONSIDERING GENERATIVE PROCESS OF F0 CONTOURS WITHIN PRODUCT OF EXPERTS FRAMEWORK

We have previously proposed a statistical fundamental frequency (F-0) prediction method that makes it possible to predict the underlying F-0 contour 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 F-0 contour was still unnatural compared with that in normal speech. One possible solution to improve the naturalness of the predicted F-0 contours would be to take account of the physical mechanism of vocal phonation. Recently a statistical model of voice F-0 contours 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 F-0 contours into the statistical F-0 prediction model. Based on the constructed model, we derive algorithms for parameter training and F-0 prediction. Experimental results revealed that the proposed method successfully outperformed our previously proposed method in terms of the naturalness of the predicted F-0 contours.