Acoustic characterization and machine prediction of perceived masculinity and femininity in adults

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Previous research has found that human voice can provide reliable information to be used for gender identification 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 characterized 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 s, 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. Next, F1 mean, F2 mean and F0 standard deviation are independent factors that share similar importance. The voice perturbation measures, including HNR, jitter and shimmer, are of lesser importance in influencing masculinity/femininity judgements.
Original languageEnglish
Pages (from-to)22-40
Number of pages19
JournalSpeech Communication
Publication statusPublished - Feb 2023

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