Prosody generation with a neural network: weighing the importance of input parameters

As an alternative to synthesis-by-rule, the use of neural networks in speech synthesis has been successfully applied to prosody generation, yet it is not known precisely which input parameters are responsible for good results. The approach presented here tries to quantify the contribution of each input parameter. This is done first by comparing the mean errors of networks trained with only one parameter each and by looking at the performance of a group of networks where each lacks one parameter. In a second approach different networks were perceptually evaluated in a pair comparison test with synthesized stimuli.