Speaker age estimation and gender detection based on supervised Non-Negative Matrix Factorization

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.