Experiments with Signal-Driven Symbolic Prosody for Statistical Parametric Speech Synthesis

This paper presents a preliminary study on the use of symbolic prosody extracted from the speech signal to improve parameters prediction on HMM-based speech synthesis. The relationship between the prosodic labelling and the actual prosody of the training data is usually ignored in the building phase of corpus based TTS voices. In this work, different systems have been trained using prosodic labels predicted from speech and compared with the conventional system that predicts those labels solely from text. Experiments have been done using data from two speakers (one male and one female). Objective evaluation performed on a test set of the corpora shows that the proposed systems improve the prediction accuracy of phonemes duration and F0 trajectories. Advantages on the use of signal-driven symbolic prosody in place of the conventional text-driven symbolic prosody, and future works about the effective use of these information in the synthesis stage of a Text To Speech systems are also described.