Regression of continuous-valued variables as a function of both categorical and continuous predictors arises in some areas of speech processing, such as when predicting prosodic targets in a text-to-speech system. In this work we investigate the use of Continuous Conditional Random Fields (CCRF) to conditionally predict F0 targets from a series of s symbolic and numerical predictive features derived from text. We derive the training equations for the model using a Least-Squares-Error criterion within a supervised framework, and evaluate the proposed system using this objective criterion against other baseline models that can handle mixed inputs, such as regression trees and ensemble of regression trees.
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