Automatic recognition of speaker age and gender based on deep neural networks

Abstract

In the given article, we present a novel approach in the paralinguistic field of age and gender recognition by speaker voice based on deep neural networks. The training and testing of proposed models were implemented on the German speech corpus aGender. We conducted experiments using different network topologies, including neural networks with fully-connected and convolutional layers. In a joint recognition of speaker age and gender, our system reached the recognition performance measured as unweighted accuracy of 48.41%. In a separate age and gender recognition setup, the obtained performance was 57.53% and 88.80%, respectively. Applied deep neural networks provide the best result of speaker age recognition in comparison to existing traditional classification methods.