Automatic ToBI prediction and alignment to speed manual labeling of prosody

Tagging of corpora for useful linguistic categories can be a time-consuming process, especially with linguistic categories for which annotation standards are relatively new, such as discourse segment boundaries or the intonational events marked in the Tones and Break Indices (ToBI) system for American English. A ToBI prosodic labeling of speech typically takes even experienced labelers from 100 to 200 times real time. An experiment was conducted to determine (1) whether manual correction of automatically assigned ToBI labels would speed labeling, and (2) whether default labels introduced any bias in label assignment. A large speech corpus of one female speaker reading several types of texts was automatically assigned default labels. Default accent placement and phrase boundary location were predicted from text using machine learning techniques. The most common ToBI labels were assigned to these locations for default tones and break type. Predicted pitch accents were automatically aligned to the mid-point of the word, while breaks and edge tones were aligned to the end of the phrase-final word. The corpus was then labeled by a group of five trained transcribers working over a period of nine months. Half of each set of recordings was labeled in the standard fashion without default labels, and the other half was presented with preassigned default labels for labelers to correct. Results indicate that labeling from defaults was generally faster than standard labeling, and that defaults had relatively little impact on label assignment.