Although Hidden Markov Model （HMM） is the most popular model for speech recognition

there has ho an intrinsic defect that

commonly assuming the output observations of a state are independent and identically-distributed（IID）

it is unable to describe the time-correlation properties of the speech phenomena. The new model proposed in this paper introduces the inter-frame correlation information into Duration-Distribution-Based HMM （DDBHMM ） by modeling separately the static and dynamic charactedstics of output observation vector sequences of speech states using parametric models and combining them into an nitegrated model. This new HMM including the inter-frame correlation information can characterize the real speech phenomena more presisely. After introducing the structure of the new model

we give the estimation formulas for the parameters of the new model and the algorithms for training and recognition.The experiment for speaker-independent recognition of all Chinese syllables shows that including the inter-frame correlation information improves the perfomance of HMM distinctively.