@phdthesis{542d27ca738442ea9fe0f6df0906050e,
title = "Machine learning methods for voice masculinity and femininity scoring and its application to individuals on the autism continuum",
abstract = "This thesis explores autism's association with voice masculinity-femininity, scrutinizing the Extreme Male Brain and androgyny accounts. It pioneers machine learning-based voice scoring, examining voice-gender links to autistic traits (adults) and autism (children). The developed machine learning models performed well in modelling voice masculinity-femininity. Minor voice gender differences were observed among adults with varying autistic traits. However, autistic children exhibited notably more masculine (boys) and less feminine (girls) voices compared to their non-autistic peers of the same age and sex. The study advances automated voice scoring and delivers insights into voice acoustics, sex differences, masculinity-femininity, and autism continuum associations.",
keywords = "autism, Extreme Male Brain, sex, voice acoustic, masculinity, machine learning, Extreme Random Forest, characterisation",
author = "Fuling Chen",
year = "2023",
doi = "10.26182/z6tv-cb95",
language = "English",
school = "The University of Western Australia",
}