Improvement of Prosody Modeling Using Semantic Role Labeling, Hybrid Feature Selection and BPSO–PSO–Optimized RNN

Abstract

A modified Elman-type recurrent neural network (RNN) is used in this study for prosody generation in natural speech synthesis application. To improve the performance of generator, three modifications are applied to the system as compared to a traditional neural model: (a) using semantic role labeling to enrich the word-level features, (b) using hybrid of genetic algorithm and ant colony optimization algorithm for feature selection to reduce the number of input features, and (c) using hybrid of particle swarm optimization (PSO) and binary PSO algorithms for optimization of weights and structure of RNN-based prosody generator model, respectively. Experimental results show the superior performance of proposed model in predicting prosodic information with lower root mean squared error and a lighter model as compared to a traditional neural model.