Claude Barras

Also published as: C. Barras


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Benchmarking multimedia technologies with the CAMOMILE platform: the case of Multimodal Person Discovery at MediaEval 2015
Johann Poignant | Hervé Bredin | Claude Barras | Mickael Stefas | Pierrick Bruneau | Thomas Tamisier
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we claim that the CAMOMILE collaborative annotation platform (developed in the framework of the eponymous CHIST-ERA project) eases the organization of multimedia technology benchmarks, automating most of the campaign technical workflow and enabling collaborative (hence faster and cheaper) annotation of the evaluation data. This is demonstrated through the successful organization of a new multimedia task at MediaEval 2015, Multimodal Person Discovery in Broadcast TV.

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The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents
Johann Poignant | Mateusz Budnik | Hervé Bredin | Claude Barras | Mickael Stefas | Pierrick Bruneau | Gilles Adda | Laurent Besacier | Hazim Ekenel | Gil Francopoulo | Javier Hernando | Joseph Mariani | Ramon Morros | Georges Quénot | Sophie Rosset | Thomas Tamisier
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we describe the organization and the implementation of the CAMOMILE collaborative annotation framework for multimodal, multimedia, multilingual (3M) data. Given the versatile nature of the analysis which can be performed on 3M data, the structure of the server was kept intentionally simple in order to preserve its genericity, relying on standard Web technologies. Layers of annotations, defined as data associated to a media fragment from the corpus, are stored in a database and can be managed through standard interfaces with authentication. Interfaces tailored specifically to the needed task can then be developed in an agile way, relying on simple but reliable services for the management of the centralized annotations. We then present our implementation of an active learning scenario for person annotation in video, relying on the CAMOMILE server; during a dry run experiment, the manual annotation of 716 speech segments was thus propagated to 3504 labeled tracks. The code of the CAMOMILE framework is distributed in open source.


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TVD: A Reproducible and Multiply Aligned TV Series Dataset
Anindya Roy | Camille Guinaudeau | Hervé Bredin | Claude Barras
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We introduce a new dataset built around two TV series from different genres, The Big Bang Theory, a situation comedy and Game of Thrones, a fantasy drama. The dataset has multiple tracks extracted from diverse sources, including dialogue (manual and automatic transcripts, multilingual subtitles), crowd-sourced textual descriptions (brief episode summaries, longer episode outlines) and various metadata (speakers, shots, scenes). The paper describes the dataset and provide tools to reproduce it for research purposes provided one has legally acquired the DVD set of the series. Tools are also provided to temporally align a major subset of dialogue and description tracks, in order to combine complementary information present in these tracks for enhanced accessibility. For alignment, we consider tracks as comparable corpora and first apply an existing algorithm for aligning such corpora based on dynamic time warping and TFIDF-based similarity scores. We improve this baseline algorithm using contextual information, WordNet-based word similarity and scene location information. We report the performance of these algorithms on a manually aligned subset of the data. To highlight the interest of the database, we report a use case involving rich speech retrieval and propose other uses.


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Annotation and analysis of overlapping speech in political interviews
Martine Adda-Decker | Claude Barras | Gilles Adda | Patrick Paroubek | Philippe Boula de Mareüil | Benoit Habert
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Looking for a better understanding of spontaneous speech-related phenomena and to improve automatic speech recognition (ASR), we present here a study on the relationship between the occurrence of overlapping speech segments and disfluencies (filled pauses, repetitions, revisions) in political interviews. First we present our data, and our overlap annotation scheme. We detail our choice of overlapping tags and our definition of disfluencies; the observed ratios of the different overlapping tags are examined, as well as their correlation with of the speaker role and propose two measures to characterise speakers’ interacting attitude: the attack/resist ratio and the attack density. We then study the relationship between the overlapping speech segments and the disfluencies in our corpus, before concluding on the perspectives that our experiments offer.


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Automatic Audio and Manual Transcripts Alignment, Time-code Transfer and Selection of Exact Transcripts
C. Barras | G. Adda | M. Adda-Decker | B. Habert | P. Boula de Mareüil | P. Paroubek
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

The present study focuses on automatic processing of sibling resources of audio and written documents, such as available in audio archives or for parliament debates: written texts are close but not exact audio transcripts. Such resources deserve attention for several reasons: they represent an interesting testbed for studying differences between written and spoken material and they yield low cost resources for acoustic model training. When automatically transcribing the audio data, regions of agreement between automatic transcripts and written sources allow to transfer time-codes to the written documents: this may be helpful in an audio archive or audio information retrieval environment. Regions of disagreement can be automatically selected for further correction by human transcribers. This study makes use of 10 hours of French radio interview archives with corresponding press-oriented transcripts. The audio corpus has then been transcribed using the LIMSI speech recognizer resulting in automatic transcripts, exhibiting an average word error rate of 12%. 80% of the text corpus (with word chunks of at least five words) can be exactly aligned with the automatic transcripts of the audio data. The residual word error rate on these 80% is less than 1%.


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Invited Talk: Processing Broadcast Audio for Information Access
Jean-Luc Gauvain | Lori Lamel | Gilles Adda | Martine Adda-Decker | Claude Barras | Langzhou Chen | Yannick de Kercadio
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics


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Transcribing with Annotation Graphs
Edouard Geoffrois | Claude Barras | Steven Bird | Zhibiao Wu
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)