Structuring baseball live games based on speech recognition using task dependent knowledge and emotion state recognition

It is a difficult problem to recognize baseball live speech because the speech is rather fast, noisy, emotional and disfluent due to rephrasing, repetition, mistakes and grammatical deviation caused by the spontaneous speaking style. To solve these problems, we propose a speech recognition method incorporating emotion state as well as baseball game knowledge, such as counting of inning, out, strike and ball. Due to this emotion state and 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. Experimental results show that the proposed approach improves the structuring and segmentation accuracy as well as keywords accuracy.