Automatic prosodic modeling for speaker and task adaptation in text-to-speech

One of the most important demands for future text-to-speech (TTS) systems is their ability to improve naturalness when embedded in a particular task or application that requires a particular speaking style for a particular speaker. We present a new prosodic modeling procedure for improving naturalness by adapting a TTS system to a new speaker and a new speaking style. The proposed procedure is an extension of our automatic data-driven methodology, to model both fundamental frequency and segmental duration. Automatic linguistic and acoustic analysis are performed on both a task dependent text corpus and the recorded material from the selected speaker.