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.