Many nonacoustic sensors are now available to augment user authentication. Devices such as the GEMS (glottal electromagnetic micro-power sensor), the EGG (electroglottograph), and the P-mic (physiological mic) all have distinct methods of measuring physical processes associated with speech production. A potential exciting aspect of the application of these sensors is that they are less influenced by acoustic noise than a microphone. A drawback of having many sensors available is the need to develop features and classification technologies appropriate to each sensor. We therefore learn feature extraction based on data. State of the art classification with Gaussian Mixture Models and Support Vector Machines is then applied for multimodal authentication. We apply our techniques to two databases--the Lawrence Livermore GEMS corpus and the DARPA Advanced Speech Encoding Pilot corpus. We show the potential of nonacoustic sensors to increase authentication accuracy in realistic situations.