1. A speech recognition apparatus, comprising: a general-corpus statistical language model that provides probability estimates of how linguistically likely a sequence of linguistic items are to occur in that sequence based on an amount of times the sequence of linguistic items occurs in text and phrases in general use; a speech recognition decoder module that requests a run-time correction module for one or more corrected probability estimates P′(z|xy) of how likely a linguistic item z is to follow a given sequence of linguistic items x followed by y; where x, y, and z are three variable linguistic items supplied from the decoder module, and the decoder module has an input to receive back the one or more domain correct probability estimates from the run-time correction module for one or more possible linguistic items z that follow the given sequence of linguistic items x followed by y; a first input in the run-time correction module configured to receive requests from the decoder module to return the one or more domain correct probability estimates for the one or more possible linguistic items z that could follow the given sequence of linguistic items x followed by y, wherein the run-time correction module is trained to linguistics of a specific domain, and is located in between the speech recognition decoder module and the statistical language model in order to adapt the probability estimates supplied by the general-corpus statistical language model to the specific domain when those probability estimates from the general-corpus statistical language model disagree by at least an established criterion based on a statistical test with the linguistic probabilities in that domain; a second input in the run-time correction module configured to receive from the statistical language model one or more probability estimates P(z|xy) of how likely are each of the possible linguistic items z that could follow the given sequence of linguistic items x followed by y; an output in the run-time correction module to return to the decoder module one or more domain corrected probability estimates P′(z|xy) of how likely are each of the possible linguistic items z that could follow the given sequence of linguistic items x followed by y; and an output module of the speech recognition system configured to provide a representation of what uttered sounds and words were inputted into the speech recognition system based on the domain corrected probability estimates, wherein the modules and models making up the speech recognition apparatus are implemented in electronic circuits, software coding, and any combination of the two, where portions implemented in software coding are stored in a format that is executable by a processor.
1. A speech recognition apparatus, comprising: a general-corpus statistical language model that provides probability estimates of how linguistically likely a sequence of linguistic items are to occur in that sequence based on an amount of times the sequence of linguistic items occurs in text and phrases in general use; a speech recognition decoder module that requests a run-time correction module for one or more corrected probability estimates P′(z|xy) of how likely a linguistic item z is to follow a given sequence of linguistic items x followed by y; where x, y, and z are three variable linguistic items supplied from the decoder module, and the decoder module has an input to receive back the one or more domain correct probability estimates from the run-time correction module for one or more possible linguistic items z that follow the given sequence of linguistic items x followed by y; a first input in the run-time correction module configured to receive requests from the decoder module to return the one or more domain correct probability estimates for the one or more possible linguistic items z that could follow the given sequence of linguistic items x followed by y, wherein the run-time correction module is trained to linguistics of a specific domain, and is located in between the speech recognition decoder module and the statistical language model in order to adapt the probability estimates supplied by the general-corpus statistical language model to the specific domain when those probability estimates from the general-corpus statistical language model disagree by at least an established criterion based on a statistical test with the linguistic probabilities in that domain; a second input in the run-time correction module configured to receive from the statistical language model one or more probability estimates P(z|xy) of how likely are each of the possible linguistic items z that could follow the given sequence of linguistic items x followed by y; an output in the run-time correction module to return to the decoder module one or more domain corrected probability estimates P′(z|xy) of how likely are each of the possible linguistic items z that could follow the given sequence of linguistic items x followed by y; and an output module of the speech recognition system configured to provide a representation of what uttered sounds and words were inputted into the speech recognition system based on the domain corrected probability estimates, wherein the modules and models making up the speech recognition apparatus are implemented in electronic circuits, software coding, and any combination of the two, where portions implemented in software coding are stored in a format that is executable by a processor. 4. The apparatus of claim 1 , further comprising: a count database coupled to the run-time correction module, where the count database is a pre-populated database specific to a linguistic domain that contains at least the number of counts that the sequence of linguistic items x followed by y occurs in overall corpus of domain-specific text from this domain analyzed C(xy), as well as the number of counts C(xyz) the N-grams (xyz), sequences of linguistic items of x followed by y followed by z, occurs in the overall corpus of domain-specific text from this analyzed domain, and where the count database returns the linguistic sequences of xy, the N-gram (xyz), and the observed counts of both C(xy) and C(xyz) in the corpus of domain-specific text analyzed when requested by the run-time correction module, but is not itself modified either during a training time or at run time.