English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Previous research has found that voices can provide reliable information tobe used for gender classification with a high level of accuracy. In socialpsychology, perceived masculinity and femininity (masculinity and femininityrated by humans) has often been considered an important feature wheninvestigating the influence of vocal features on social behaviours. Whileprevious studies have characterised the acoustic features that contributed toperceivers' judgements of speakers' masculinity or femininity, there is limitedresearch on developing a machine masculinity/femininity scoring model andcharacterizing the independent acoustic factors that contribute to perceivers'masculinity and femininity judgements. In this work, we first propose a machinescoring model of perceived masculinity/femininity based on the Extreme RandomForest and then characterize the independent and meaningful acoustic factorsthat contribute to perceivers' judgements by using a correlation matrix basedhierarchical clustering method. Our results show that the machine ratings ofmasculinity and femininity strongly correlated with the human ratings ofmasculinity and femininity when we used an optimal speech duration of 7seconds, with a correlation coefficient of up to .63 for females and .77 formales. Nine independent clusters of acoustic measures were generated from ourmodelling of femininity judgements for female voices and eight clusters werefound for masculinity judgements for male voices. The results revealed that,for both genders, the F0 mean is the most important acoustic measure affectingthe judgement of acoustic-related masculinity and femininity. The F3 mean, F4mean and VTL estimators were found to be highly inter-correlated and appearedin the same cluster, forming the second most significant factor in influencingthe assessment of acoustic-related masculinity and femininity.

Voice Gender Scoring and Independent Acoustic Characterization of Perceived Masculinity and Femininity