Previous research has found that voices can provide reliable information to be used for gender classification with a high level of accuracy. In social psychology, perceived masculinity and femininity (masculinity and femininity rated by humans) has often been considered an important feature when investigating the influence of vocal features on social behaviours. While previous studies have characterised the acoustic features that contributed to perceivers' judgements of speakers' masculinity or femininity, there is limited research on developing a machine masculinity/femininity scoring model and characterizing the independent acoustic factors that contribute to perceivers' masculinity and femininity judgements. In this work, we first propose a machine scoring model of perceived masculinity/femininity based on the Extreme Random Forest and then characterize the independent and meaningful acoustic factors that contribute to perceivers' judgements by using a correlation matrix based hierarchical clustering method. Our results show that the machine ratings of masculinity and femininity strongly correlated with the human ratings of masculinity and femininity when we used an optimal speech duration of 7 seconds, with a correlation coefficient of up to .63 for females and .77 for males. Nine independent clusters of acoustic measures were generated from our modelling of femininity judgements for female voices and eight clusters were found for masculinity judgements for male voices. The results revealed that, for both genders, the F0 mean is the most important acoustic measure affecting the judgement of acoustic-related masculinity and femininity. The F3 mean, F4 mean and VTL estimators were found to be highly inter-correlated and appeared in the same cluster, forming the second most significant factor in influencing the assessment of acoustic-related masculinity and femininity.
翻译:先前的研究发现,声音可以提供可靠的信息,用于性别分类,并具有高度准确性。在社会心理学中,在调查声势特征对社会行为的影响时,男性和女性通常被视为一个重要特征。虽然先前的研究对有助于人们判断演讲者雄性或女性的声学特征的声学特征进行了定性,但在开发机器雄性/女性评分模型和定性独立声学因素的特征方面的研究有限。在社会心理学中,被认为的男性和女性(被人类评分的男性和女性)和女性(被人类评分的男性和女性)女性(被男性和女性评分的男性和女性)往往被视为一个重要特征。在这项工作中,我们首先提出一个基于极端随机森林的男性/女性声学特征/女性特征的评分模型模型模型模型模型,然后通过使用基于关联性矩阵的矩阵方法来描述有助于人们判断的判断的独立和有意义的声学因素。 我们发现,男性和女性性别的评分与人类的评分有强烈性关系。 当我们使用最优的音质判断时, 女性的音质判断和女性的判分值数据组中, 和女性的代判分数是第8秒的。