Prosodically modifying speech for unit selection speech synthesis databases

This paper investigates the practical limits of artificially increasing the prosodic richness of a unit selection database by transforming the prosodic realization of constituent sentences. The resulting high-quality transformed sentences are added to the database as new material. We examine in detail one of the most challenging prosodic transformations, namely converting statements into yes/no questions. Such transformations can require very large prosodic modifications while at the same time there is a need to retain as much naturalness of the signal as possible. Our data-driven approach relies on learning templates of pitch contours for different stress patterns of interrogative sentences from training data and later on applying these template pitch contours on unseen statements to generate the corresponding questions. We examine experimentally how the modified signals contribute to the perceived synthesis quality of the resulting database when compared with baseline unmodified databases.