Adolescent age estimation using voice features.

In this paper, a method for evaluating the chronological age of adolescents on the basis of their voice signal is presented. For every examined child, the vowels a, e, i, o and u were recorded in extended phonation. Sixty voice parameters were extracted from each recording. Voice recordings were supplemented with height measurement in order to check if it could improve the accuracy of the proposed solution. Predictor selection was performed using the LASSO (least absolute shrinkage and selection operator) algorithm. For age estimation, the random forest (RF) for regression method was employed and it was tested using a 10-fold cross-validation. The lowest absolute error (0.37 year ± 0.28) was obtained for boys only when all selected features were included into prediction. In all cases, the achieved accuracy was higher for boys than for girls, which results from the fact that the change of voice with age is larger for men than for women. The achieved results suggest that the presented approach can be employed for accurate age estimation during rapid development in children.

Prediction of menarcheal status of girls using voice features.

A method for evaluating the menarcheal status of girls on the basis of their voice features is presented in the paper. The registration procedure consists of voice recording and measuring 20 anthropological features. The input feature vector is a combination of voice and anthropometric parameters, counting 220 features. The optimal set of parameters was selected using five different methods: Method A - stepwise regression (first forward, then backward regression) performed on features with statistically different means/medians; Method B - stepwise regression (forward and backward) on all features, with age; Method C - stepwise regression as in B; including age, Method D - all features with statistically different means/medians, Method E - all features excluding age. For classification purposes three methods were employed: random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA) classifier. They were tested with 10-fold cross validation. The classification accuracy for RF using only voice features is higher than using only anthropometric data: 86.86% vs. 81.02% respectively. For the other two classifiers, the results do not show as large a difference: 80.60% vs. 82.80% for SVM and 80.66% vs. 82.34% for LDA. The advantage of voice features is more noticeable with sensitivity: 91.92% vs. 83.06% for RF. The obtained results suggest that the presented method can be used for automatic recognition of girls' menarcheal status using voice signal.