Human listeners are able to extract word sequences from running speech. Priming is an implicit memory effect in which exposure to one stimulus influences a response to another stimulus. In a noise environment, the auditory priming effect can significantly release speech from masking. There are two various kinds of masking that lead to speechrecognition difficulty under noise environment, including informational masking and energetic masking. Quantitative evaluating the auditory priming effect in the noise environment still poses a problem that the introduction of acoustic background noise induced stress causes speech recognition algorithms to fail. This study investigated a new quantitative computational method for the auditory priming effect in the speech recognition under noise conditions. This new computational method can help to improve the understanding of human auditory intelligence and then further provide more effective means to facilitate speech recognition of the selective target message in noise.