An efficient multi-path acoustic model based on database likelihoods for spontaneous speech recognition is presented. Although a multipath phone HMM that has several models for different target in parallel is considered effective to express multi-style or speed-variant nature of spontaneous speech, assuming various model to match at every time for all phones may cause mismatch of unintended mo- del, and spoil the model constraints. We propose defining a multipath model that has several different state resolutions only for the distortive phones selectively. The phone set is selected through an analysis of the likelihoods and duration times of phone segments in a spoken dialogue corpus using automatic viterbi alignment. Experiments on three test-sets showed that our multi-path model based on the phone selection can achieve better accuracy than a simple single-path model, whereas a full multi-path model without phone selection causes much degradation of accuracy.