This paper proposes a model of a spectrum target prediction mechanism and a preprocessing method for automatic speech recognition by using the model to cope with coarticulation. The model is constructed to predict particular spectra for each phoneme, that is phoneme target, and to keep their spectra constant in each phoneme interval. The method is evaluated by four measures: spectrum sequence stability—Are predicted spectrum sequence in each phoneme interval fixed?; intra-category spectrum variation—Is a variation of predicted spectra in each phoneme category small?; inter-category spectrum variation—Is phoneme category pair far apart measuring by the Mahalanobis distance?; and lengths of transitional sounds—How long is the duration of wrong recognized results in a phoneme interval. Experimental results indicate that predicted spectra throughout the model are stabilized in each phoneme interval. Moreover, by using the method, intra-category variation decreases and inter-category variation increases. The results also indicate that the model recovers vowel characteristics neutralized by coarticulation at the spectral transition portion and decreases the duration of transitional sounds. Consequently, the spectrum target prediction model implemented as a speech recognition preprocessor reduces recognition error rates.