Corpus description of the ESTER Evaluation Campaign for the Rich Transcription of French Broadcast News
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This paper presents the audio corpus developed in the framework of the ESTER evaluation campaign of French broadcast news transcription systems.This corpus includes 100 hours of manually annotated recordings and 1,677 hours of non transcribed data.The manual annotations include the detailed verbatim orthographic transcription, the speaker turns and identities, information about acoustic conditions, and name entities.Additional resources generated by automatic speech processing systems, such as phonetic alignments and word graphs, are also described.
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Existing French corpora (BREF120, ESTER, Quaero) are not applicable to this study because they do not include calls for help, were acquired from younger speakers, and were recorded in studio or broadcast news.
“However these corpora are not applicable to our study because they do not include calls for help. Furthermore, they were all acquired from younger speakers and were either recorded in studio by speakers reading texts or extracted from broadcast news.”
From Making Emergency Calls More Accessible to Older Adults Through a Hands-free Speech Interface in the House (2019) · cites this paper as Galliano et al., 2006
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Research works printed but not linked: 4
- C. Barras, E. Geoffrois, Z. Wu, and M. Liberman. Tran- scriber: a free tool for segmenting, labeling and tran- scribing speech. In Proc. Language Evaluation and Re- sources Conference, pp. 1373-1376, 1998.
- S. Galliano, E. Geoffrois, G. Gravier, J.-F. Bonastre, D. Mostefa, and K. Choukri. The ESTER evaluation cam- paign for the rich transcription of French broadcast news. In Interspeech -Eurospeech, 2005.
- G. Gravier, J.-F. Bonastre, S. Galliano, E. Geoffrois, K. Mc Tait, and K. Choukri. The ESTER evaluation campaign of rich transcription of French broadcast news. In Proc. Language Evaluation and Resources Conference, 2004. Hub 1999. Broadcast News Workshop, 1999. http://www.nist.gov/speech/publications/darpa99.
- A. Martin and M. Przybocki. The NIST Speaker Recogni- tion Evaluations: 1996-2001. In Speaker Odyssey, 2001. RT 2004. Spring 2004 Rich Transcription Workshop, 2004. http://www.nist.gov/speech/tests/rt/.
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Laurençon, H., Saulnier, L., Wang, T., Akiki, C., del Moral, A. V., Scao, T. L., Von Werra, L., Mou, C., Ponferrada, E. G., Nguyen, H., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03915 · Cited by 61 · see citing works
Vacher, M., Aman, F., Rossato, S., Portet, F., & Lecouteux, B. (2019) · ACM Transactions on Accessible Computing · doi:10.1145/3310132 · Cited by 10 · see citing works
- Authors
- Galliano, S., Geoffrois, E., Gravier, G., Bonastre, J.-F., Mostefa, D., & Choukri, K.
- Affiliations
- Direction Générale de l'Armement (FR)
- Evaluations and Language resources Distribution Agency (FR)
- Venue
- Proceedings of the Language Resources and Evaluation Conference
- Published
- 2006-05
- Pages
- 139-142
- ISSN
- 2522-2686
- OpenAlex
- W64472890
- Cited by
- 109
- References
- 0
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 2 · 2022: 6 · 2021: 4 · 2020: 5 · 2019: 5 · 2018: 6 · 2017: 2 · 2016: 12 · 2015: 12 · 2014: 6 · 2013: 1 · 2012: 4
- FWCI
- 3.29
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Language Resources and Evaluation Conference (LREC) (publishedVersion, cc-by-nc)
- Language Resources and Evaluation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Linguistic Studies and Language Acquisition
- Keywords
- speech transcription, audio corpus, manual annotation, speaker turns, named entity recognition, phonetic alignment, word graphs, acoustic conditions, speech recognition evaluation
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