  This paper presents a software allowing to describe voices using a continuous
Voice Femininity Percentage (VFP). This system is intended for transgender
speakers during their voice transition and for voice therapists supporting them
in this process. A corpus of 41 French cis- and transgender speakers was
recorded. A perceptual evaluation allowed 57 participants to estimate the VFP
for each voice. Binary gender classification models were trained on external
gender-balanced data and used on overlapping windows to obtain average gender
prediction estimates, which were calibrated to predict VFP and obtained higher
accuracy than $F_0$ or vocal track length-based models. Training data speaking
style and DNN architecture were shown to impact VFP estimation. Accuracy of the
models was affected by speakers' age. This highlights the importance of style,
age, and the conception of gender as binary or not, to build adequate
statistical representations of cultural concepts.
