| Resumo: | This dissertation focuses on the identification of disfluent sequences and their distinct structural regions. Reported experiments are based on audio segmentation and prosodic features, calculated from a corpus of university lectures in European Portuguese, containing about 32 hours of speech and about 7.7% of disfluencies. The set of features automatically extracted from the forced alignment corpus proved to be discriminant of the regions contained in the production of a disfluency. The best results concern the detection of the interregnum, followed by the detection of the interruption point. Several machine learning methods have been applied, but experiments show that Classification and Regression Trees usually outperform the other methods. The set of most informative features 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. We have also conducted initial experiments on automatic detecting filled pauses, the most frequent disfluency type. For now, only force aligned transcripts were used, since the ASR system is not well adapted to this domain. This study is a step towards automatic detection of filled pauses for European Portuguese using prosodic features. Future work will extend this study for fully automatic transcripts, and will also tackle other domains, also exploring extended sets of linguistic features.
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