  InaGVAD is an audio corpus collected from 10 French radio and 18 TV channels
categorized into 4 groups: generalist radio, music radio, news TV, and
generalist TV. It contains 277 1-minute-long annotated recordings aimed at
representing the acoustic diversity of French audiovisual programs and was
primarily designed to build systems able to monitor men's and women's speaking
time in media. inaGVAD is provided with Voice Activity Detection (VAD) and
Speaker Gender Segmentation (SGS) annotations extended with overlap, speaker
traits (gender, age, voice quality), and 10 non-speech event categories.
Annotation distributions are detailed for each channel category. This dataset
is partitioned into a 1h development and a 3h37 test subset, allowing fair and
reproducible system evaluation. A benchmark of 6 freely available VAD software
is presented, showing diverse abilities based on channel and non-speech event
categories. Two existing SGS systems are evaluated on the corpus and compared
against a baseline X-vector transfer learning strategy, trained on the
development subset. Results demonstrate that our proposal, trained on a single
- but diverse - hour of data, achieved competitive SGS results. The entire
inaGVAD package; including corpus, annotations, evaluation scripts, and
baseline training code; is made freely accessible, fostering future advancement
in the domain.
