Utterance copy for Klatt´s speech synthesizer using genetic algorithm

This work describes the current version of newGASpeech, a framework centered on analysis-by-synthesis and genetic algorithms for automatically estimating the input parameters of Klatt´s speech synthesizer. The goal is to speed up the process of speech imitation (or utterance copy), where one has to find the model parameters that lead to a synthesized speech sounding close enough to the target speech. The main focus is speech pathology research. The proposed system is compared with WinSnoori and it outperforms this baseline by a large margin with respect to, for example, mean square error and PESQ scores.