嗓音性别评分与男性气质和女性气质感知的独立声学表征
Voice Gender Scoring and Independent Acoustic Characterization of Perceived Masculinity and Femininity
摘要：
先前的研究发现，声音可以提供可靠的信息，用于性别分类，具有很高的准确性。在社会心理学中，感知到的男性气质和女性气质（由人类评定的男性气质和女性气质）通常被认为是调查声音特征对社会行为影响的重要特征。虽然以前的研究已经表征了有助于感知者对说话者的男性气质或女性气质的判断的声学特征，但关于开发机器男性气质/女性气质评分模型和表征有助于感知者的男性气质和女性气质判断的独立声学因素的研究有限。在这项工作中，我们首先提出了一个基于极端随机森林的感知男性/女性的机器评分模型，然后通过使用基于相关矩阵的层次聚类方法来表征独立的和有意义的声学因素，有助于感知者的判断。我们的研究结果表明，当我们使用7秒的最佳语音持续时间时，机器对男性气质和女性气质的评分与人类对男性气质和女性气质的评分密切相关，女性的相关系数高达0.63，男性为0.77。九个独立的集群的声学措施，从我们的女性声音的女性化判断和八个集群的男性化判断男性的声音。结果表明，对于两种性别，F0平均值是最重要的声学措施影响的判断声学相关的男性和女性。的F3平均值，F4平均值和ESTA估计被发现是高度相互关联的，并出现在同一个集群，形成第二个最重要的因素，影响评估声学相关的男性和女性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
