  Sex classification of children's voices allows for an investigation of the
development of secondary sex characteristics which has been a key interest in
the field of speech analysis. This research investigated a broad range of
acoustic features from scripted and spontaneous speech and applied a
hierarchical clustering-based machine learning model to distinguish the sex of
children aged between 5 and 15 years. We proposed an optimal feature set and
our modelling achieved an average F1 score (the harmonic mean of the precision
and recall) of 0.84 across all ages. Our results suggest that the sex
classification is generally more accurate when a model is developed for each
year group rather than for children in 4-year age bands, with classification
accuracy being better for older age groups. We found that spontaneous speech
could provide more helpful cues in sex classification than scripted speech,
especially for children younger than 7 years. For younger age groups, a broad
range of acoustic factors contributed evenly to sex classification, while for
older age groups, F0-related acoustic factors were found to be the most
critical predictors generally. Other important acoustic factors for older age
groups include vocal tract length estimators, spectral flux, loudness and
unvoiced features.
