Improvement of word recognition results by trigram model

A trigram language model based on word categories is introduced in order to improve word recognition results by use of linguistic information. A trigram model based on word sequences requires a lot of memory and training samples to store and estimate its probabilities. To avoid these almost unsolvable problems, a trigram model of words whose probabilities are estimated from the trigram of categories and word occurrence probabilities in the dictionary is introduced. The probabilities of the trigram of categories and the word probabilities in the dictionary are estimated using the Brown Corpus Text Database[1]. This trigram model is efficiently applied to improve word recognition results using a dynamic programming technique. Moreover, probabilities of special word sequences (frozen word sequences) are extracted from the Brown Corpus Text Database and these probabilities are also integrated in the dynamic programming algorithm. Word recognition through speaker adaptation is carried out using three input speakers from the IBM office correspondence task database[3]. The word recognition rate was 80.9%. The trigram model improves the word recognition rate to 89.1%.