{"url":"/dataset/emodb-dataset","name":"EmoDB Dataset","full_name":"Berlin Database of Emotional Speech","description_markdown":"The EMODB database is the freely available German emotional database. The database is created by the Institute of Communication Science, Technical University, Berlin, Germany. Ten professional speakers (five males and five females) participated in data recording. The database contains a total of 535 utterances. The EMODB database comprises of seven emotions: 1) anger; 2) boredom; 3) anxiety; 4) happiness; 5) sadness; 6) disgust; and 7) neutral. The data was recorded at a 48-kHz sampling rate and then down-sampled to 16-kHz.\r\n\r\nCitation:  \r\nFelix Burkhardt, Astrid Paeschke, Miriam Rolfes, Walter Sendlmeier und Benjamin Weiss\r\nA Database of German Emotional Speech\r\nProceedings Interspeech 2005, Lissabon, Portugal","description_withheld":null,"homepage":"http://emodb.bilderbar.info/docu/","introduced_date":"2005-10-01","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Speech Recognition","url":"/task/speech-recognition","datasets_with_task":"/datasets/task/speech-recognition"},{"name":"Speech Emotion Recognition","url":"/task/speech-emotion-recognition","datasets_with_task":"/datasets/task/speech-emotion-recognition"},{"name":"Emotional Speech Synthesis","url":"/task/emotional-speech-synthesis","datasets_with_task":"/datasets/task/emotional-speech-synthesis"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"German","url":"/datasets/language/german"}],"variants":["EmoDB Dataset"],"data_loaders":[{"repo":"https://github.com/felixbur/nkululeko","url":"https://nkululeko.readthedocs.org","frameworks":["pytorch"]}],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/speech-emotion-recognition-on-emodb-dataset","task":"Speech Emotion Recognition","dataset_variant":"EmoDB Dataset","rows":1,"metrics":["Accuracy","F1"],"first_row_in_archive_order":{"model":"VQ-MAE-S-12 (Frame) + Query2Emo","paper":"/paper/a-vector-quantized-masked-autoencoder-for","metrics":{"Accuracy":"90.2","F1":"0.891"},"code_links":[{"title":"samsad35/VQ-MAE-S-code","url":"https://github.com/samsad35/VQ-MAE-S-code"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-vector-quantized-masked-autoencoder-for","title":"A vector quantized masked autoencoder for speech emotion recognition","date":"2023-04-21","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}