  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.
