Abstract
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.
Abstract (translated)
本文介绍了一种使用连续的声音女性化百分比（VFP）描述声音的软件。这个系统旨在为变性说话者在其变声过程中使用，并为声音治疗师提供支持。一个由41名法国同性恋和跨性别说话者组成的语料库进行了记录。感知评估让57名参与者估计每个声音的VFP。二元性别分类模型在平衡的外部数据上进行训练，并在重叠窗口上使用，以获得平均性别预测估计，这些估计经校准可预测VFP，并获得比$F_0$或基于语音轨道长度的模型更高的准确性。培训数据中的说话方式和DNN架构被证明会影响VFP估计的准确性。说话者的年龄也影响了模型的准确性。这突出了风格、年龄和性别概念的二元或非二元性质对文化概念进行适当统计表示的重要性。
URL
https://arxiv.org/abs/2404.15176
