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In many criminal cases, evidence might be in the form of telephone conversations or tape recordings. Therefore, law enforcement agencies have been concerned about accurate methods to profile different characteristics of a speaker from recorded voice patterns, which facilitate the identification of a criminal. This paper proposes a new approach for speaker gender detection and age estimation, based on a hybrid architecture of Weighted Supervised Non-Negative Matrix Factorization (WSNMF) and General Regression Neural Network (GRNN). Evaluation results on a corpus of read and spontaneous speech in Dutch confirms the effectiveness of the proposed scheme.