Intonation modeling using FFNN for syllable based Bengali text to speech synthesis

This paper proposes an intonation model using feed forward neural network (FFNN) for syllable based text to speech (TTS) synthesis system for an Indian language Bengali. The features used to model the neural network include set of positional, contextual and phonological features. The proposed intonation model predicts three F0 values correspond to initial, middle and final positions of each syllable. These three F0 values captures the broad shape of the F0 contour of the syllable. The prediction performance of the neural network model is compared with the Classification and Regression Tree (CART) model which was used by Festival for building the TTS. Both CART and FFNN models are evaluated by means of objective measures such as average prediction error (μ), root mean squared error (RMSE) and correlation coefficient (γ_{X, Y}). The models are also evaluated using subjective listening tests.