Learning continuous representation of text for phone duration modeling in statistical parametric speech synthesis

In this paper, we investigate the usage of a continuous representation based approach of the feature vector derived from input text to predict the phone durations in a Text to Speech(TTS) system. We pose the problem of predicting the duration as a data driven statistical transformation from the input text onto the feature space. First we present a method to map both the categorical and numeric features that are typically used into a continuous numeric representation and then model it as a form of Matrix Factorization to improve the representation. The proposed system is evaluated based on Root Mean Squared Error(RMSE) as the objective measure and Mean Opinion Score(MOS) as the subjective measure. We find that the system performs on par with the state of the art duration modeling systems both subjectively and objectively.