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Nowadays, concatenative method is used in most modern TTS systems to produce artificial speech. The most important challenge in this method is choosing appropriate unit for creating database. This unit must warranty smoothness and high quality speech, and also, creating database for it must reasonable and inexpensive. For example, syllable, phoneme, allophone, and, diphone are appropriate units for all-purpose systems. In this paper, we implemented three synthesis systems for Kurdish language based on syllable, allophone, and diphone and compare their quality using subjective testing.

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Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system's state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that 1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, 2) the robustness testing is the most cost-efficient strategy, and 3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms.