This paper deals with joint gender identification and age group classification from speech, aimed at improving the functionalities of Interactive Voice Response Systems. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities over a wide range of applications, among them, speech processing problems based on features extraction and selection. A comparative study of various neural network architectures and sizes is presented to gather knowledge about performance dependence on the network architecture and the number of free parameters. The classification framework was trained and evaluated using Mozilla's 'Common Voice' dataset, an open and crowdsourced speech corpus. The results are promising, with the best systems achieving a gender identification error lower than 2% and an age group classification error lower than 20%.
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