Prediction of prosodic phrase boundaries considering variable speaking rate

The paper proposes a model for predicting the prosodic phrase boundaries of speech with variable speaking rates. Speakers can produce a sentence in several ways without altering its meaning or naturalness, i.e., a sequence of words can have a number of prosodic phrase boundaries. There are many factors which influence the variability of prosodic phrasing, such as syntactic structure, focus, speaker differences, speaking rate and the need to breathe. We adopt dependency grammar, similar to link grammar, to efficiently combine speaking rates. The proposed model reduced prosodic phrase boundary prediction error by 20% compared to the model using only syntactic information. We show a potential way to make use of a read speech corpus in the training of prosodic phrasing for spontaneous speech. The proposed model is expected to make synthesized speech more natural and improve the robustness of spontaneous speech recognition.