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This paper introduces a novel model-constrained, data-driven method for generating fundamental frequency contours in Japanese text-to-speech synthesis. In the training phase, the parameters of a command-response F 0 contour generation model are learned by a prediction module, which can be a neural network or a set of binary regression trees. The input features consist of linguistic information related to accentual phrases that can be automatically derived from text, such as the position of the accentual phrase in the utterance, number of morae, accent type, and parts-of-speech. In the synthesis phase, the prediction module is used to generate appropriate values of model parameters. The use of the parametric model restricts the degrees of freedom of the problem, facilitating data-driven learning. Experimental results show that the method makes it possible to generate quite natural F 0 contours with a relatively small training database

Nonlinear Shape Prior from Kernel Space for Geometric

The Geometric Active Contour (GAC) framework, which utilizes image information, has proven to be quite valuable for performing segmentation. However, the use of image information alone often leads to poor segmentation results in the presence of noise, clutter or occlusion. The introduction of shapes priors in the contour evolution proved to be an effective way to circumvent this issue. Recently, an algorithm was proposed, in which linear PCA (principal component analysis) was performed on training sets of data and the shape statistics thus obtained were used in the segmentation process. This approach was shown to convincingly capture small variations in the shape of an object

Modeling And Generation Of Accentual Phrase F 0

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 7, JULY 1998 Comments on "Geodesic Saliency of

In a recent paper on morphological image segmentation [1], Najman and Schmitt introduce the powerful concept of edge dynamics