Discriminative training for concatenative speech synthesis

In this letter, we propose an approach to train the cost functions used for unit selection in concatenative speech synthesis. We first view the unit selection as a classification problem, and we apply the discriminative training technique, which is found to be an efficient way to perform parameter estimation in speech recognition. Instead of defining an objective function that accounts for the subjective speech quality, we take the classification error as the objective function to be optimized. The classification error is approximated by a smooth function, and the relevant parameters are updated by means of the gradient descent technique.