GMM-based speaker age and gender classification in Czech and Slovak

Abstract

The paper describes an experiment with using the Gaussian mixture models (GMM) for automatic classification of the speaker age and gender. It analyses and compares the influence of different number of mixtures and different types of speech features used for GMM gender/age classification. Dependence of the computational complexity on the number of used mixtures is also analysed. Finally, the GMM classification accuracy is compared with the output of the conventional listening tests. The results of these objective and subjective evaluations are in correspondence.K e y w o r d s: GMM classifier, spectral and prosodic features of speech, speaker gender and age classification

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

“…Indeed, by comparing females included in the YA and OA groups as well as males included in the YA and OA groups, in separate analyses, we have examined the pure effect of ageing on voice. Our findings fully agree with previous reports demonstrating the effect of ageing on the human voice [ 24 , 25 , 26 , 27 , 28 , 33 , 34 , 35 , 36 , 37 , 38 ]. Early studies based on the qualitative/perceptual evaluation of voice recordings have demonstrated that physiologic ageing leads to several changes in specific characteristics of the human voice [ 1 ].…”

“…In our study, by applying the ROC curve analysis, we demonstrated in detail the high accuracy of our machine learning analysis in demonstrating age-related changes in the human voice. Our results fit in well with previous studies applying automatic classifiers based on machine learning analysis [ 24 , 25 , 26 , 27 , 28 , 33 , 34 , 35 , 36 , 37 , 38 ]. More in detail, our machine learning algorithm has achieved higher results than those obtained on the INTERSPEECH 2010 age and gender sub-challenge feature set [ 33 , 34 ].…”

“…More in detail, our machine learning algorithm has achieved higher results than those obtained on the INTERSPEECH 2010 age and gender sub-challenge feature set [ 33 , 34 ]. Among machine learning algorithms, the standard and hybrid versions of the SVM (e.g., SVM-GMM) are thought to be both consistent and accurate [ 33 , 34 , 35 , 38 , 73 ]. In our study, SVM achieved relatively high performance with an accuracy of 95.3% in age recognition and of 95.5% in gender recognition, showing comparable or even better results than those obtained in previous reports [ 33 , 34 , 35 , 38 , 73 ].…”

“…Among machine learning algorithms, the standard and hybrid versions of the SVM (e.g., SVM-GMM) are thought to be both consistent and accurate [ 33 , 34 , 35 , 38 , 73 ]. In our study, SVM achieved relatively high performance with an accuracy of 95.3% in age recognition and of 95.5% in gender recognition, showing comparable or even better results than those obtained in previous reports [ 33 , 34 , 35 , 38 , 73 ]. When comparing our methodological approach to those previously used, it is important to consider that we started with a large dataset of features (more than 6000), adopting dedicated ranking and feature selection algorithms [ 33 , 34 , 35 , 36 , 37 , 38 , 73 ].…”

“…More in detail, machine learning can be applied to predict outcomes from recurring patterns of features within various types of multidimensional data sets [ 32 ]. Several authors have applied automatic classifiers based on machine learning analysis on voice recordings to classify healthy subjects according to their age and gender [ 24 , 25 , 26 , 27 , 28 , 33 , 34 , 35 , 36 , 37 , 38 ]. More recently, to further improve the overall accuracy of the machine learning analysis, several studies have included an increasing number of voice features in the datasets [ 24 , 25 , 26 , 27 , 28 ] and compared the performance of different machine learning algorithms [ 37 , 38 ].…”