6. A computer implemented method for generating translations from speech from different sources, said method comprising: a) inputting acoustic training data with associated phonetic transcriptions; b) identifying transitional diphone elements employed in said training data which span from the middle of one phone to the middle of the next phone. c) for each of said diphone elements, generating a corresponding diphone model comprising a sequence of states, each state from each model defining a class; d) training said diphone models from said training data; e) labeling training data and class identifiers; f) accumulating statistics from said training data identifying within-class and between-class variances and combining said training data statistics from across all sources using expanded matrices; g) generating composite eigen vector matrix from said expanded matrices and splitting said composite eigen vector matrix into sections corresponding to respective sources; h) transforming said training data using respective sections of said eigen vector matrix and reducing the number of dimensions; i) repeating steps c through h using transformed training data for step d until transform matrices converge; j) generating and storing vocabulary models using transformed training data; k) converting speech to be recognized to a succession of data frames; l) transforming the frame data using the eigen vector matrix obtained in step g; and m) comparing the transformed frame data with said stored vocabulary models to identify those models which best match the frame data.
8. A computer method as set forth in claim 6 wherein said states are probability distribution funtions.