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 Posteriori adaptation (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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Bocklet, T., Maier, A., & Nöth, E. (2008). Age determination of children in preschool and primary school age with GMM-based supervectors and support vector machines/regression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5246 LNAI, pp. 253–260). https://doi.org/10.1007/978-3-540-87391-4_33
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