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In this paper, we present a maximum a posteriori (MAP) eigenvoice speaker adaptation approach to the self-adaptation system. The proposed MAP eigenvoice is developed by introducing a probability density for the eigenvoice coefficients such as MAPLR adaptation. And we make a self-adaptation system which is useful to public user, because user does not need to speak several sentences for adaptation. In self-adaptation system we use only one utterance that will be recognized, so we use eigenvoice adaptation with MAP criterion that is most robust adaptation algorithm for very small adaptation data. In a series of self-adaptation experiments on the Korean connected digit recognition task, we demonstrate that the proposed approach achieves a good performance for a very small amount of adaptation data.