{"url":"/dataset/inagvad","name":"inaGVAD","full_name":"InaGVAD : a Challenging French TV and Radio Corpus annotated for Voice Activity Detection and Speaker Gender Segmentation","description_markdown":"InaGVAD is a Voice Activity Detection (VAD) and Speaker Gender Segmentation (SGS) dataset designed for representing the acoustic diversity of French TV and Radio programs. InaGVAD detailed description, together with a benchmark of 6 freely available VAD systems and 3 SGS systems, is provided in a paper presented in LREC-COLING 2024.\r\n\r\nInaGVAD contains 277 1-minute-long annotated recordings, partitioned into a 1h development and 3h37 test subset, allowing fair and reproducible system evaluation. Evaluation scripts provided with the corpus provide performance estimates in the same conditions as the 6 VAD and 3 SGS systems presented in the associated paper. Recordings were collected from 10 French radio and 18 TV channels categorized into 4 groups associated to diverse acoustic conditions : generalist radio, music radio, news TV, and generalist TV.\r\n\r\nInaGVAD provides an extended VAD and SGS annotation scheme, allowing to describe systems diverse abilities based on :\r\n* Speaker Traits categories\r\n** 3 Genders : Female, Male, I Don't Know (IDK)\r\n** 3 Age groups : Young (prepubescent), Adult, Ederly (Senior)\r\n** 3 Speech Qualities : standard, interjections (ah, oh, eg, aie), atypical (crying, laughing or shouted speech, ill person voice, artificially distorted voices, vocal performance, monster voice...)\r\n*10 Non-Speech event categories : Applause, environmental noise, hubbub, jingle, foreground music, background music, respiration, non-intelligible laughers, other, empty\r\n\r\nThe entire inaGVAD package; including corpus, annotations, evaluation scripts, and baseline training code; is made freely accessible, fostering future advancement in the domain.","description_withheld":null,"homepage":"https://github.com/ina-foss/InaGVAD","introduced_date":"2024-05-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/inagvad-a-challenging-french-tv-and-radio","title":"InaGVAD : a Challenging French TV and Radio Corpus Annotated for Speech Activity Detection and Speaker Gender Segmentation","first_author":"David Doukhan","url":null},"license":{"name":"INA GCU","url":"https://www.ina.fr/recherche/dataset-project"},"modalities":[{"name":"Audio","url":"/datasets/modality/audio"},{"name":"Speech","url":"/datasets/modality/speech"},{"name":"Music","url":"/datasets/modality/music"}],"tasks":[{"name":"Gender Prediction","url":"/task/gender-prediction","datasets_with_task":"/datasets/task/gender-prediction"},{"name":"Gender Classification","url":"/task/gender-classification","datasets_with_task":"/datasets/task/gender-classification"},{"name":"Gender Bias Detection","url":"/task/gender-bias-detection","datasets_with_task":"/datasets/task/gender-bias-detection"}],"languages":[{"name":"French","url":"/datasets/language/french"}],"variants":["inaGVAD"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}