Incremental network generation in word recognition

It is well known that a network representation of templates has many advantages; however, generating a network by hand is an impossible task for a large vocabulary database. This paper describes an automatic incremental network generation algorithm for speaker independent isolated word recognition. Because of its incremental nature, it is possible to add new words to the network at any time; because of its complete freedom from human intervention, it is language and vocabulary independent. By applying this technique to speaker-independent recognition, recognition accuracy of 99% was obtained for the digits, and 91.92% was obtained for the alphabets.