Car manufacturers are faced with a new challenge. While a new generation of “digital natives” becomes a new customer group, the problem of aging society is still increasing. This emphasizes the need of providing ?exible in-car dialog that take into account the speci?c needs and preferences of the respective user (group). Along the lines of this year’s Interspeech motto “Spoken Language Processing for All”, we address the question how we ?nd out which group the current user belongs to. We present a GMM/SVM-supervector system (Gaussian MixtureModel combined with Support VectorMachine) for speaker age and gender recognition, a technique that is adopted from state-of-the-art speaker recognition research. We furthermore describe an experimental study with the aim to evaluate the performance of the system as well as to explore the selection of parameters.