A CRF-based Approach to Automatic Disfluency Detection in a French Call-Centre Corpus. Dutrey, C.; Clavel, C.; Rosset, S.; Vasilescu, I.; and Adda-Decker, M. Technical Report 2014.
A CRF-based Approach to Automatic Disfluency Detection in a French Call-Centre Corpus [pdf]Paper  A CRF-based Approach to Automatic Disfluency Detection in a French Call-Centre Corpus [link]Website  abstract   bibtex   
In this paper, we present a Conditional Random Field based approach for automatic detection of edit disfluencies in a conversational telephone corpus in French. We define dis-fluency patterns using both linguistic and acoustic features to perform disfluency detection. Two related tasks are considered : the first task aims at detecting the disfluent speech portion proper or reparandum, i.e. the portion to be removed if we want to improve the readability of transcribed data ; in the second task, we aim at identifying also the corrected portion or repair which can be useful in follow-up discourse and dialogue analyses or in opinion mining. For these two tasks, we present comparative results as a function of the involved type of features (acoustic and/or linguistic). Generally speaking, best results are obtained by CRF models combining both acoustic and linguistic features.
@techreport{
 title = {A CRF-based Approach to Automatic Disfluency Detection in a French Call-Centre Corpus},
 type = {techreport},
 year = {2014},
 keywords = {Index Terms : disfluencies,conditional random fields,conver-sational speech,spontaneous speech},
 websites = {https://gforge.inria.fr/projects/discretize4crf/.},
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 abstract = {In this paper, we present a Conditional Random Field based approach for automatic detection of edit disfluencies in a conversational telephone corpus in French. We define dis-fluency patterns using both linguistic and acoustic features to perform disfluency detection. Two related tasks are considered : the first task aims at detecting the disfluent speech portion proper or reparandum, i.e. the portion to be removed if we want to improve the readability of transcribed data ; in the second task, we aim at identifying also the corrected portion or repair which can be useful in follow-up discourse and dialogue analyses or in opinion mining. For these two tasks, we present comparative results as a function of the involved type of features (acoustic and/or linguistic). Generally speaking, best results are obtained by CRF models combining both acoustic and linguistic features.},
 bibtype = {techreport},
 author = {Dutrey, Camille and Clavel, Chloé and Rosset, Sophie and Vasilescu, Ioana and Adda-Decker, Martine}
}
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