Automated Sex Classification of Children's Voices and Changes in Differentiating Factors with Age

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.

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

Previous research has found that voices can provide reliable information for gender classification with a high level of accuracy. In social psychology, perceived vocal masculinity and femininity has often been considered as an important feature on social behaviours. While previous studies have characterised acoustic features that contributed to perceivers' judgements of speakers' vocal masculinity or femininity, there is limited research on building an objective masculinity/femininity scoring model and characterizing the independent acoustic factors that contribute to the judgements of speakers' vocal masculinity or femininity. In this work, we firstly propose an objective masculinity/femininity scoring system based on the Extreme Random Forest and then characterize the independent and meaningful acoustic factors contributing to perceivers' judgements by using a correlation matrix based hierarchical clustering method. The results show the objective masculinity/femininity ratings strongly correlated with the perceived masculinity/femininity ratings when we used an optimal speech duration of 7 seconds, with a correlation coefficient of up to .63 for females and .77 for males. 9 independent clusters of acoustic measures were generated from our modelling of femininity judgements for female voices and 8 clusters were found for masculinity judgements for male voices. The results revealed that, for both sexes, the F0 mean is the most critical acoustic measure affects the judgement of vocal masculinity and femininity. The F3 mean, F4 mean and VTL estimators are found to be highly inter-correlated and appeared in the same cluster, forming the second significant factor. 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.