Automatic corpus-based training of rules for prosodic generation in text-to-speech

In this paper, we discuss a methodology for automatic prosodic modeling in Text-to-Speech (TTS) systems. The proposed methodology can be seen as a data-driven strategy to train prosodic rules from the automatic analysis of a specific text and its related speech material. Therefore, our corpus-based training procedure is based on an automatic linguistic analysis of the text and on an acoustic analysis of the speech using automatic speech recognition techniques. Together with the automatic derivation of prosodic rules, our method can be easily extended to obtain specific grammar categories suitable for accurate prosodic modeling of specific tasks. Evaluation results over two different applications and speaker styles, reveal that the proposed automatic prosodic generation procedure is able to provide a noticeable increase in naturalness when adapting TTS system to a new speaker and a new speaking style.