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In mobile speech communication applications, wind noise can lead to a severe reduction of speech quality and intelligibility. Since the performance of speech enhancement algorithms using acoustic microphones tends to substantially degrade in extremely challenging scenarios, auxiliary sensors such as contact microphones can be used. Although contact microphones offer a much lower recorded wind noise level, they come at the cost of speech distortion and additional noise components. Aiming at exploiting the advantages of acoustic and contact microphones for wind noise reduction, in this paper we propose to extend conventional single-microphone dictionary-based speech enhancement approaches by simultaneously modeling the acoustic and contact microphone signals. We propose to train a single speech dictionary and two noise dictionaries and use a relative transfer function to model the relationship between the speech components at the microphones. Simulation results show that the proposed approach yields improvements in both speech quality and intelligibility compared to several baseline approaches, most notably approaches using only the contact microphones or only the acoustic microphone.