Automatic detection and annotation of disfluencies in spoken French corpora. Christodoulides, G. & Avanzi, M. In Interspeech 2015. Proceedings of the 16th Annual Conference of the Internationational Speech Communication Association, pages 1849–1853, 2015. ![Automatic detection and annotation of disfluencies in spoken French corpora [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex In this paper we propose a multi-step system for the semi-automatic detection and annotation of disfluencies in spoken corpora. A set of rules, statistical models and machine learning techniques are applied to the input, which is a transcription aligned to the speech signal. The system uses the results of an automatic estimation of prosodic, part-of-speech and shallow syntactic features. We present a detailed coding scheme for simple disfluencies (filled pauses, mispronunciations, false starts, drawls and intra-word pauses), structured disfluencies (repetitions, deletions, substitutions, insertions) and complex disfluencies. The system is trained and evaluated on a transcribed corpus of spontaneous French speech, consisting of 112 different speakers and balanced for speaker age and sex, covering 14 different varieties of French spoken in Belgium, France and Switzerland.
@inproceedings{christodoulides_automatic_2015,
Author = {Christodoulides, George and Avanzi, Mathieu},
Booktitle = {Interspeech 2015. Proceedings of the 16th Annual Conference of the Internationational Speech Communication Association},
Date = {2015},
Date-Modified = {2018-07-20 18:49:25 +0000},
Eventdate = {2015-09-06/2015-09-10},
Keywords = {labelling and annotation, language resources, disfluencies, French, phonetics, speaking styles, speech technology, spontaneous speech, speech corpora},
Location = {Dresden, Germany},
Pages = {1849--1853},
Title = {Automatic detection and annotation of disfluencies in spoken French corpora},
Url = {http://www.isca-speech.org/archive/interspeech_2015/i15_1849.html},
Year = {2015},
Abstract = {In this paper we propose a multi-step system for the semi-automatic detection and annotation of disfluencies in spoken corpora. A set of rules, statistical models and machine learning techniques are applied to the input, which is a transcription aligned to the speech signal. The system uses the results of an automatic estimation of prosodic, part-of-speech and shallow syntactic features. We present a detailed coding scheme for simple disfluencies (filled pauses, mispronunciations, false starts, drawls and intra-word pauses), structured disfluencies (repetitions, deletions, substitutions, insertions) and complex disfluencies. The system is trained and evaluated on a transcribed corpus of spontaneous French speech, consisting of 112 different speakers and balanced for speaker age and sex, covering 14 different varieties of French spoken in Belgium, France and Switzerland.},
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Downloads: 0
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