Hybrid of Evolutionary and Swarm Intelligence Algorithms for Prosody Modeling in Natural Speech Synthesis

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.