STATISTICAL LANGUAGE MODEL FORMING DEVICE AND SPEECH RECOGNITION DEVICE

摘要

PROBLEM TO BE SOLVED: To improve the prediction accuracy and reliability of a transition probability by setting part-of-speech bigrams into an initial state and executing two kinds of state sepn. of word separation and integration of concatenated words from part-of-speech classes, thereby forming a statistical language model of variable length N-grams. SOLUTION: A word hypothesis converging section 6 converges the word hypothesis in such a manner that the word hypothesis of the same words of the equal end time and the different start time is represented by the one word hypothesis having the highest likelihood by referencing the statistical language model 22. The statistical language model 22 used here separates the words reliable with the words alone from the part-of-speech classes and handles these words as the independent classes on the basis of the bigram (N=2) between the part-of-speech classes. As to frequently appearing word strings, the model integrates these word strings and handles the same as one class, thereby making the expression of the long word concatenation possible. The statistical language model 22 having the characteristics of the part-of-speech bigrams and the variable length word N-grams in combination is thus formed.