PURPOSE: To reduce the loading of a central processing unit(CPU) by using an algorithm which makes the probability score of current state compared with a prescribed critical value and a hypothesis including a state having the probability score lower than the critical value abandoned. CONSTITUTION: The score of current state with information is calculated, the score is compared with the prescribed critical value, the position of slot of a usable scoring buffer is found out, and the information on the current state is stored in the usable slot. Next, the back pointer of slot of the scoring buffer is set and a current time index is compared with a time index with each scoring buffer slot so that the usable slot can be found out. the current time index is analyzed while being related with the time indexes of all the slots along the best route to the current state. Afterwards, the probability score of the next current state is calculated and the algorithm is repeated until all the states are completed. Thus, the loading of the CPU can be reduced by efficiently performing simplification while using the critical score.
EFFICIENT SIMPLIFIED ALGORITHM OF NEGATIVE MARKOV MODEL SPEECH RECOGNITION