  This article focuses on overlapped speech and gender detection in order to
study interactions between women and men in French audiovisual media (Gender
Equality Monitoring project). In this application context, we need to
automatically segment the speech signal according to speakers gender, and to
identify when at least two speakers speak at the same time. We propose to use
WavLM model which has the advantage of being pre-trained on a huge amount of
speech data, to build an overlapped speech detection (OSD) and a gender
detection (GD) systems. In this study, we use two different corpora. The DIHARD
III corpus which is well adapted for the OSD task but lack gender information.
The ALLIES corpus fits with the project application context. Our best OSD
system is a Temporal Convolutional Network (TCN) with WavLM pre-trained
features as input, which reaches a new state-of-the-art F1-score performance on
DIHARD. A neural GD is trained with WavLM inputs on a gender balanced subset of
the French broadcast news ALLIES data, and obtains an accuracy of 97.9%. This
work opens new perspectives for human science researchers regarding the
differences of representation between women and men in French media.
