Automatic Period Segmentation of Oral French
Natalia Kalashnikova, Loïc Grobol, Iris Eshkol-Taravella, François Delafontaine
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.- Anthology ID:
- 2020.lrec-1.785
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6389–6394
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.785
- DOI:
- Cite (ACL):
- Natalia Kalashnikova, Loïc Grobol, Iris Eshkol-Taravella, and François Delafontaine. 2020. Automatic Period Segmentation of Oral French. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6389–6394, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Automatic Period Segmentation of Oral French (Kalashnikova et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.lrec-1.785.pdf