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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.
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https://hal.archives-ouvertes.fr/hal-02770725
Contributor : Loïc Grobol <>
Submitted on : Thursday, June 4, 2020 - 11:58:15 AM
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  • HAL Id : hal-02770725, version 1

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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⟩

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