Age Determination of Children in Preschool and Primary School Age with GMM-Based Supervectors and Support Vector Machines/Regression

Authors:
Tobias Bocklet;Andreas Maier;Elmar Nöth
Affiliations:
Institute of Pattern Recognition, University of Erlangen-Nuremberg, Germany;Institute of Pattern Recognition, University of Erlangen-Nuremberg, Germany;Institute of Pattern Recognition, University of Erlangen-Nuremberg, Germany
Venue:
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
Year:
2008

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Abstract

This paper focuses on the automatic determination of the age of children in preschool and primary school age. For each child a Gaussian Mixture Model(GMM) is trained. As training method the Maximum A Posterioriadaptation (MAP) is used. MAP derives the speaker models from a Universal Background Model(UBM) and does not perform an independent parameter estimation. The means of each GMM are extracted and concatenated, which results in a so-called GMM supervector. These supervectors are then used as meta features for classification with Support Vector Machines(SVM) or for Support Vector Regression(SVR). With the classification system a precision of 83 % was achieved and a recall of 66 %. When the regression system was used to determine the age in years, a mean error of 0.8 years and a maximal error of 3 years was obtained. A regression with a monthly accuracy brought similar results.