@ARTICLE{Sheikhan, author = {Sheikhan, Mansour and }, title = {Hybrid of Evolutionary and Swarm Intelligence Algorithms for Prosody Modeling in Natural Speech Synthesis }, volume = {8}, number = {2}, abstract ={To reduce the number of input features to a prosody generator in natural speech synthesis application, a hybrid of an evolutionary algorithm and a swarm intelligence-based algorithm is used for feature selection (FS) in this study. The input features to FS unit are word-level and syllable-level linguistic features. The word-level features include punctuation information, part-of-speech tags, semantic indicators, and length of the words. The syllable-level features include the phonemic structure and position indicator of the current syllable in a word. A modified Elman-type dynamic neural network (DNN) is used for prosody generation in this study. The output layer of this DNN provides prosody information at the syllable-level including pitch contour, log-energy level, duration information, and pause data. Simulation results show that the prosody information is predicted with an acceptable error by this hybrid soft-computing method as compared to Elman-type neural network prosody generator and binary gravitational search algorithm-based FS unit. }, URL = {http://ijict.itrc.ac.ir/article-1-69-en.html}, eprint = {http://ijict.itrc.ac.ir/article-1-69-en.pdf}, journal = {International Journal of Information and Communication Technology Research}, doi = {}, year = {2016} }