Analysis and Automatic Classification of Some Discourse Particles on a Large Set of French Spoken Corpora

  • Denis JouvetEmail author
  • Katarina Bartkova
  • Mathilde Dargnat
  • Lou Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10583)


In French, quite a number of words and expressions are frequently used as discourse particles in spoken language, especially in spontaneous speech. The semantic load of these words or expressions differ whether they are used as discourse particles or not. Therefore, the correct identification of their discourse function remains of great importance. In this paper the distribution of the discourse function (or not discourse function), and of the detailed discourse functions of some of these words, is studied on a large set of French corpora ranging from prepared speech (e.g. storytelling and broadcast news) to spontaneous speech (e.g. interviews and interactions between people). The paper is focused on a subset of discourse particles that are recurrent in the considered corpora. The discourse function of a few thousand occurrences of these words have been manually annotated. A statistical analysis of the functions of the words is presented and discussed with respect to the types of spoken corpora. Finally, some statistics with respect to a few prosodic correlates of the discourse particles are presented, as well as some results of automatic classification and detection of the word function (discourse particle or not) using prosodic features.


Discourse particles French language Prosodic parameters Discourse function statistics Discourse particle detection 



This work has been carried out in the framework of the ProsodCorpus operation supported by the CPER LCHN (Contrat Plan Etat Région “Langues, Connaissances et Humanités Numériques”). Some experiments presented in this paper have been carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Denis Jouvet
    • 1
    • 2
    • 3
    Email author
  • Katarina Bartkova
    • 4
    • 5
  • Mathilde Dargnat
    • 4
    • 5
  • Lou Lee
    • 4
    • 5
  1. 1.InriaVillers-lès-NancyFrance
  2. 2.Université de Lorraine, LORIA, UMR 7503Villers-lè-NancyFrance
  3. 3.CNRS, LORIA, UMR 7503Villers-lès-NancyFrance
  4. 4.Université de Lorraine, ATILF, UMR 7118NancyFrance
  5. 5.CNRS, ATILF, UMR 7118NancyFrance

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