{"url":"/dataset/semaine","name":"SEMAINE","full_name":null,"description_markdown":"The **SEMAINE** videos dataset contains spontaneous data capturing the audiovisual interaction between a human and an operator undertaking the role of an avatar with four personalities: Poppy (happy), Obadiah (gloomy), Spike (angry) and Prudence (pragmatic). The audiovisual sequences have been recorded at a video rate of 25 fps (352 x 288 pixels). The dataset consists of audiovisual interaction between a human and an operator undertaking the role of an agent (Sensitive Artificial Agent). SEMAINE video clips have been annotated with couples of epistemic states such as agreement, interested, certain, concentration, and thoughtful with continuous rating (within the range [1,-1]) where -1 indicates most negative rating (i.e: No concentration at all) and +1 defines the highest (Most concentration). Twenty-four recording sessions are used in the Solid SAL scenario. Recordings are made of both the user and the operator, and there are usually four character interactions in each recording session, providing a total of 95 character interactions and 190 video clips.\r\n\r\nSource: [ROBUST MODELING OF EPISTEMIC MENTAL STATES](https://arxiv.org/abs/2005.13982)","description_withheld":null,"homepage":"https://ibug.doc.ic.ac.uk/resources/semaine-database2/","introduced_date":"2012-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent","first_author":null,"url":"https://doi.org/10.1109/T-AFFC.2011.20"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"Audio","url":"/datasets/modality/audio"}],"tasks":[{"name":"Emotion Recognition in Conversation","url":"/task/emotion-recognition-in-conversation","datasets_with_task":"/datasets/task/emotion-recognition-in-conversation"}],"languages":[],"variants":["SEMAINE"],"data_loaders":[],"num_papers_in_archive":54,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/emotion-recognition-in-conversation-on-2","task":"Emotion Recognition in Conversation","dataset_variant":"SEMAINE","rows":3,"metrics":["MAE (Valence)","MAE (Arousal)","MAE (Expectancy)","MAE (Power)"],"first_row_in_archive_order":{"model":"DialogueGCN","paper":"/paper/dialoguegcn-a-graph-convolutional-neural","metrics":{"MAE (Arousal)":"0.161","MAE (Expectancy)":"0.168","MAE (Power)":"7.68","MAE (Valence)":"0.157"},"code_links":[{"title":"SenticNet/conv-emotion","url":"https://github.com/SenticNet/conv-emotion"},{"title":"KomorebiLHX/Emotion-Recognition-in-Conversations","url":"https://github.com/KomorebiLHX/Emotion-Recognition-in-Conversations"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hierarchical-pre-training-for-sequence","title":"Hierarchical Pre-training for Sequence Labelling in Spoken Dialog","date":"2020-09-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/dialoguegcn-a-graph-convolutional-neural","title":"DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation","date":"2019-08-30","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dialoguernn-an-attentive-rnn-for-emotion","title":"DialogueRNN: An Attentive RNN for Emotion Detection in Conversations","date":"2018-11-01","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}