It is a difficult problem to recognize baseball live speech because the speech is rather fast, noisy and disfluent due to rephrasing, repetition, mistake and grammatical deviation caused by spontaneous speaking style. To solve these problems, we propose in this paper a speech recognition method of incorporating the baseball game knowledge such as counting of inning, out, strike and ball. Due to this task-dependent knowledge, the proposed method can effectively prevent speech recognition errors. This method is formalized in the framework of probability theory and implemented in the conventional speech decoding (Viterbi) algorithm. The experimental results showed that the proposed approach improved the structuring situation segmentation accuracy as well as keywords accuracy.