Combining Acoustic, Lexical, and Syntactic Evidence for Automatic Unsupervised Prosody Labeling

Automatic labeling of prosodic events in speech has potentially significant implications for spoken language processing applications, and has received much attention over the years, especially after the introduction of annotation standards such as ToBI. Current labeling techniques are based on supervised learning, relying on the availability of a corpus that is annotated with the prosodic labels of interest in order to train the system. However, creating such resources is an expensive and time-consuming task. In this paper, we examine an unsupervised labeling algorithm for accent (prominence) and prosodic phrase boundary detection at the linguistic syllable level, and evaluate their performance on an standard, manually annotated corpus. We obtain labeling accuracies of 77.8% and 88.5% for the accent and boundary labeling tasks, respectively. These figures compare well against previously reported performance levels for supervised labelers.