Influence of Language Parameters Selection on the Coarticulation of the Phonemes for Prosody Training in TTS by Neural Networks

This contribution describes the influence of the Czech language parameters selection on the coarticulation of the phonemes for the modelling of prosody features by the artificial neural network (ANN) in a text-to-speech (TTS) synthesis. The GUHA method and neural network pruning can be used for this reason. In our work we analyzed the errors between the target and calculated values of F₀ and D from the point of view of the different context of speech units. The context of three phonemes combinations CCC, VVC, VCV, CVV, VCC, CCV, and CVC (C = consonant, V = vowel) were analyzed for the determination of a next improvement of prosody. The qualitative criteria have been found in this contribution.