Serial Speakers: a Dataset of TV Series
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Abstract (arXiv)
For over a decade, TV series have been drawing increasing interest, both from the audience and from various academic fields. But while most viewers are hooked on the continuous plots of TV serials, the few annotated datasets available to researchers focus on standalone episodes of classical TV series. We aim at filling this gap by providing the multimedia/speech processing communities with Serial Speakers, an annotated dataset of 161 episodes from three popular American TV serials: Breaking Bad, Game of Thrones and House of Cards. Serial Speakers is suitable both for investigating multimedia retrieval in realistic use case scenarios, and for addressing lower level speech related tasks in especially challenging conditions. We publicly release annotations for every speech turn (boundaries, speaker) and scene boundary, along with annotations for shot boundaries, recurring shots, and interacting speakers in a subset of episodes. Because of copyright restrictions, the textual content of the speech turns is encrypted in the public version of the dataset, but we provide the users with a simple online tool to recover the plain text from their own subtitle files.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2021MovieCuts: A New Dataset and Benchmark for Cut Type Recognitioncited by 26
- 2021Towards Personalised and Document-level Machine Translation of Dialoguecited by 4
- 2023Decoding the Popularity of TV Series: A Network Analysis Perspectivecited by 0
- 2026Overcoming Copyright Barriers in Corpus Distribution Through Non-Reversible Hashingcited by 0
- 2026Overcoming Copyright Barriers in Corpus Distribution Through Non-Reversible Hashing25 Apr 2026
- 2023Decoding the Popularity of TV Series: A Network Analysis Perspective4 Jul 2023
- 2021MovieCuts: A New Dataset and Benchmark for Cut Type Recognition12 Sep 2021
- 2021Towards Personalised and Document-level Machine Translation of Dialogue11 Feb 2021