Disfluency detection based on prosodic features for university lectures

This paper focuses on the identification of disfluent sequences and their distinct structural regions, based on acoustic and prosodic features. Reported experiments are based on a corpus of university lectures in European Portuguese, with roughly 32h, and a relatively high percentage of disfluencies (7.6%). The set of features automatically extracted from the corpus proved to be discriminant of the regions contained in the production of a disfluency. Several machine learning methods have been applied, but the best results were achieved using Classification and Regression Trees (CART). The set of features which was most informative for cross-region identification encompasses word duration ratios, word confidence score, silent ratios, and pitch and energy slopes. Features such as the number of phones and syllables per word proved to be more useful for the identification of the interregnum, whereas energy slopes were most suited for identifying the interruption point.