Abstract: The event detection-based method has become state of the art technique in Automatic Speech Recognition (ASR).The differences in speaking rate may impair the adaptation ability of acoustical models, On account of this, A novel adaptation algorithm is proposed in this paper, which adjust the frame and step size in the front end of the system with the cell of one utterance, after adaptation, the speaking rate consistent with the average rate of the speech corpus and decreasing it’s effect in model training. In addition, this method calculates the angle between vectors of the posterior probability to get the speed of the testing set, which eased the burden of system compared to that by training models. The algorithm was used in the pre-processing before the phonological features detection stage, and then with the nonlinear transformation, we put them as the observation of Hidden Markov Models based phone recognition systems. After the adaptation approach, the average frame of one phone in an utterance becomes constant and the dynamic range decreases, therefore the phoneme classification rate increase about 1.3%.