Conventional concatenated training of phoneme HMMs can approximate the output probability structure of the HMMs. However, it causes an essential problem that the output probability structure of the HMMs becomes blurred, because HMMs are trained over the whole speech data of a given sentence. This paper describes a method to solve this problem and to make the output probability structure sharp, by restricting the training section to the proper section associated with the phoneme, still keeping the advantages of the conventional concatenated HMM training. Four kinds of experiments were carried out and the proposed method showed a 5.6% improvement of the recognition rate of the phonemes included in the continuously spoken sentences, compared to the conventional concatenated phoneme HMM training.>