TVD: a reproducible and multiply aligned TV series dataset

• Main Authors: Roy, Anindya, Guinaudeau, Camille, Bredin, Hervé, Barras, Claude
• Other Authors: Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), ANR-10-CORD-0103,QCOMPERE,Consortium Quaero pour la Reconnaissane Multimodale des Personnes(2010), ANR-12-CHRI-0006,CAMOMILE,Annotation collaborative de documents multi-modaux, multi-lingues et multi-média(2012)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2014
• Subjects: TV Series; transcripts; episode outlines; [INFO]Computer Science [cs]; [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]; Iceland
• Online Access: https://hal.science/hal-01690279; https://hal.science/hal-01690279/document; https://hal.science/hal-01690279/file/751_Paper.pdf

International audience 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.