Efficient pruning algorithm for hidden markov model speech recognition

摘要

A method for efficient pruning which reduces central processing unit loading during real time speech recognition. A CPU uses a predetermined threshold for discarding not useful or necessary information. Useful information is stored in an available scoring buffer slot. A slot is said to be available if its last-time field does not equal the current time-index. To prevent pruning of a slot in the best path, the current time-index has to be propagated to all slots in the best path. This back-propagation increases CPU loading and is alleviated by referring the backpointer of a state within a model to its start state.