Skip to Main content Skip to Navigation
Conference papers

Automatic Period Segmentation of Oral French

Abstract : Natural Language Processing in oral speech segmentation is still looking for a minimal unit to analyze. In this work, we present a comparison of two automatic segmentation methods of macro-syntactic periods which allows to take into account syntactic and prosodic components of speech. We compare the performances of an existing tool Analor (Avanzi, Lacheret-Dujour, Victorri, 2008) developed for automatic segmentation of prosodic periods and of CRF models relying on syntactic and / or prosodic features. We find that Analor tends to divide speech into smaller segments and that CRF models detect larger segments rather than macro-syntactic periods. However, in general CRF models perform better results than Analor in terms of F-measure.
Complete list of metadata

Cited literature [17 references]  Display  Hide  Download
Contributor : Loïc Grobol <>
Submitted on : Thursday, June 4, 2020 - 11:58:15 AM
Last modification on : Thursday, July 1, 2021 - 5:46:02 PM
Long-term archiving on: : Friday, December 4, 2020 - 8:46:50 PM


Files produced by the author(s)


  • HAL Id : hal-02770725, version 1


Natalia Kalashnikova, Loïc Grobol, Iris Eshkol-Taravella, François Delafontaine. Automatic Period Segmentation of Oral French. 12th International Conference on Language Resources and Evaluation, May 2020, Marseille, France. ⟨hal-02770725⟩



Record views


Files downloads