We present an approach to style specific phrasing for Text-to- Speech (TTS) systems. We formulate the problem of phrase break prediction (or phrasing) as generation of a sequence of breaks (B) and non-breaks (NB) after each word in a sentence. We use prosodic breaks in speech data to build shallow parses over corresponding text. We then learn a grammar that can predict these shallow prosodic parses from text. We then combine this prosodic phrasing information with other word level features in a CART tree to predict where phrase breaks should be inserted in new text. We show that a model built to target a specific reading style can predict phrase breaks more accurately than the standard generic model.
Bibliographic reference. Parlikar, Alok / Black, Alan W. (2011): "A grammar based approach to style specific phrase prediction", In INTERSPEECH-2011, 2149-2152.