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Title: Disfluency Detection Based on Prosodic Features for University Lectures
Authors: Medeiros, Henrique
Moniz, Helena
Batista, Fernando
Trancoso, Isabel
Nunes, Luís
Keywords: prosodic features
automatic disfluency detection
corpus of university lectures
machine learning
Issue Date: 30-Jul-2013
Abstract: 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.
Peer reviewed: Sim
Appears in Collections:CTI-CRI - Comunicações a conferências internacionais

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