A memory management method for a large word network

To improve the performance of continuous speech recognition, it is effective to incorporate grammatical knowledge of task into a word network of a FSN (finite state network) form. But, recently , some of them requires huge memory, so we introduce an ef ficient memory management method for a large word network; distributed FSN model and hiearchical memory model. The system keeps the word network divided to small sub-networks, and activates each sub-network when necessary. Using this method, we can recognize continuously spoken sentences of Japanese addresses, which are made of 390K geographic names, with only 5.6 Mbytes local memory in average.