Patent Document ID: 5467425
Application ID: 08023543

Base Claim:
1. A computer based language modelling system receiving data in the form of training text divided into a series of n-grams, each n-gram comprising a series of "n" words, each n-gram having an associated count, the history of an n-gram being represented by the initial n-1 words of the n-gram, comprising: a language modelling means for determining a conditional probability of a predicted word given the previous (n-1) words, comprising: a memory means for storing the data; a separating means coupled to said memory means for examining each word within each n-gram and classifying each n-gram into one of one or more classes based upon one or more words in a given n-gram, each class having one or more similar n-grams associated with said class, said similar n-grams having the same predicted word and x previous words, where x varies from (n-1) to zero, to associate each n-gram with exactly one of said one or more classes, each class is identified with one of one or more sets based upon the value of x used when determining the class of the n-gram; a factor means coupled to the output of said separating means and to said memory means for determining a factor for each of said one or more classes, said factor representing the relative strength of predicting said predicted word given the previous (n-1) words, the value of each factor being approximately equal to the ratio of the sum of the counts of each n-gram associated with a given class over the sum of the counts of all (n-1)-grams which when followed by said predicted word would belong to said given class; and a conditional probability means coupled to the output of said factor means for determining said conditional probability of the occurrence of said predicted word given that a particular sequence of (n-1) previous words have occurred using said factors, said conditional probability approximately equal to the ratio of a first factor, said first factor associated with the class that a given n-gram is associated with, said given n-gram equal to said predicted word and the history of said predicted word, the history equal to a particular sequence of (n-1) previous words, over the sum of one or more factors, said one or more factors associated with all of the classes of n-grams obtained by using said particular sequence of (n-1) words followed by any word of the vocabulary.

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Claim 2:
2. The system of claim 1, further comprising: a user interface for accepting input data in the form of spoken sounds; a signal processor coupled to the output of said user interface for creating a series of feature vector signals based upon said spoken sounds; a labelling means coupled to the output of said signal processor for labelling said series of feature vector signals with a label from a label alphabet to produce a series of labelled feature vector signals; and a matching means coupled to the output of said labelling means for creating and updating one or more sequences of word-series hypotheses which represent said sounds input into said user interface which were spoken up to a given point in time, comprising: a second memory means for storing said sequences of word-series hypotheses, a search controlling means coupled to an acoustic matching means, said language modelling means, and to said second memory means for controlling the sequence of performance of said acoustic matching means and of said language modelling means, and for controlling the inputs into said language modelling means in the form of signals representing a current word from a word choice set obtained from said acoustic matching means, a sequence of (n-1) previous words obtained from said second memory means, said (n-1) previous words being the last (n-1) words in one of the sequences of word-series hypotheses, said sequences of word-series hypotheses resulting from previous outputs of said matching means, said acoustic matching means coupled to said search controlling means for comparing an acoustic parameter vector signal with said labelled feature vector signals to reduce the number of word choices that likely represent said labelled feature vector signals, said word choices are output to the search controlling means which provides each of said word choices as an input to said language modelling means, said language modelling means determines a score for each of the word choices, said score is an estimate of the conditional probability of each of said word choices given that each of the sequences of word-series hypotheses have occurred, each of said sequences of word-series hypotheses is input into said language modelling means from said search controlling means.