The IBM expressive text-to-speech synthesis system for American English

Expressive text-to-speech (TTS) synthesis should contribute to the pleasantness, intelligibility, and speed of speech-based human-machine interactions which use TTS. We describe a TTS engine which can be directed, via text markup, to use a variety of expressive styles, here, questioning, contrastive emphasis, and conveying good and bad news. Differences in these styles lead us to investigate two approaches for expressive TTS, a "corpus-driven" and a "prosodic-phonology" approach. Each speaker records 11 h (excluding silences) of "neutral" sentences. In the corpus-driven approach, the speaker also records 1-h corpora in each expressive style; these segments are tagged by style for use during search, and decision trees for determining f 0 contours and timing are trained separately for each of the neutral and expressive corpora. In the prosodic-phonology approach, rules translating certain expressive markup elements to tones and break indices (ToBI) are manually determined, and the ToBI elements are used in single f 0 and duration trees for all expressions. Tests show that listeners identify synthesis in particular styles ranging from 70% correctly for "conveying bad news" to 85% for "yes-no questions". Further improvements are demonstrated through the use of speaker-pooled f 0 and duration models