13. A method for a speech recognition system, comprising: improving an accuracy of probability estimates of how linguistically likely a sequence of linguistic items are to occur in that sequence from an underlying statistical language model by adding a correction module trained on different or more extensive data than the underlying statistical language model, in which the correction module systematically corrects the statistical language model estimates, where those probability estimates from the statistical language model significantly disagree with evidence available to the correction module; identifying all special N-grams (Yz) those for which the probability estimate P(z|Y) from the statistical language model is implausible given actual counts of C(Yz) and C(Y) that have been identified and recorded in a count database, where z is a specific linguistic unit that occurs in a given context of Y and Y is a specific sequence of one or more of other linguistic units; establishing a threshold value (t) to determine whether the evidence available to the correction module being a difference in the observed counts in the count database C(Yz) from an estimated amount of counts of the N-gram context of Yz in a background training data derived from the underlying statistical language model E(Yz); in response to a request from a decoder module of the speech recognition system, the correction module returning estimates from the statistical language model with a normalization factor applied to those estimates when an actual number of counts of the N-gram context of Yz in the corpus of specific text analyzed by the correction module C(Yz) are not significantly different, by being within the threshold value t, from the estimated amount of counts of the N-gram from the statistical language model; and in response to a request from a decoder module of the speech recognition system, the correction module returning for each special N-grams (Yz), a corrected probability estimate associated with that special N-gram based on counts of the linguistic sequence found in the count database when the actual number of counts of the N-gram sequence of Yz in the corpus of text analyzed by the correction module C(Yz) are significantly different from the estimated amount of counts of the N-gram from the statistical language model, and discarding then the statistical language model's returned probabilities.
13. A method for a speech recognition system, comprising: improving an accuracy of probability estimates of how linguistically likely a sequence of linguistic items are to occur in that sequence from an underlying statistical language model by adding a correction module trained on different or more extensive data than the underlying statistical language model, in which the correction module systematically corrects the statistical language model estimates, where those probability estimates from the statistical language model significantly disagree with evidence available to the correction module; identifying all special N-grams (Yz) those for which the probability estimate P(z|Y) from the statistical language model is implausible given actual counts of C(Yz) and C(Y) that have been identified and recorded in a count database, where z is a specific linguistic unit that occurs in a given context of Y and Y is a specific sequence of one or more of other linguistic units; establishing a threshold value (t) to determine whether the evidence available to the correction module being a difference in the observed counts in the count database C(Yz) from an estimated amount of counts of the N-gram context of Yz in a background training data derived from the underlying statistical language model E(Yz); in response to a request from a decoder module of the speech recognition system, the correction module returning estimates from the statistical language model with a normalization factor applied to those estimates when an actual number of counts of the N-gram context of Yz in the corpus of specific text analyzed by the correction module C(Yz) are not significantly different, by being within the threshold value t, from the estimated amount of counts of the N-gram from the statistical language model; and in response to a request from a decoder module of the speech recognition system, the correction module returning for each special N-grams (Yz), a corrected probability estimate associated with that special N-gram based on counts of the linguistic sequence found in the count database when the actual number of counts of the N-gram sequence of Yz in the corpus of text analyzed by the correction module C(Yz) are significantly different from the estimated amount of counts of the N-gram from the statistical language model, and discarding then the statistical language model's returned probabilities. 14. The method of claim 13 , wherein the correction module returns corrected probability estimates for each special N-gram of Yz based on a smoothed count of C′(Yz) divided by a smoothed count of C′(Y), where the smoothed counts mathematically readjust the probability estimate associated with that special N-gram so the returned corrected probability estimate are neither assigned a zero percent probability nor have numeric value which was divided by the number zero, and the underlying statistical language model is a general corpus statistical language model in which the correction module trained to a specific domain systematically corrects the general corpus statistical language model estimates, where those probability estimates from the statistical language model significantly disagree with the linguistic probabilities in that domain.