1. A computer implemented method for generating translations from speech which accommodates different sources using a common set of vocabulary models, said method comprising: a) inputting, from multiple sources, acoustic training data with associated phonetic transcriptions; b) identifying multi-phone elements employed in said training data; c) for each of said multi-phone elements, generating a corresponding multi-phone model comprising a sequence of states, each state from each model defining a class; d) training said multi-phone models from said training data independently of source; e) labeling training data with source and class identifiers; f) accumulating source specific statistics from said training data identifying within-class and between-class variances; g) combining said training data statistics from across all sources using expanded matrices; h) generating a composite eigen vector matrix from said expanded matrices; i) splitting said composite eigen vector matrix into sections corresponding to respective sources for use in transforming speech data obtained from the respective sources; j) transforming training data from respective sources using corresponding eigen vector matrix sections; k) repeating steps c through j using transformed training data for step d until transform matrices converge; l) generating and storing vocabulary models using transformed training data; m) converting speech to be recognized from a given source to a succession of data frames; n) transforming the frame data using the corresponding eigen vector matrix section obtained in step i; and o) comparing the transformed frame data with said stored vocabulary models to identify those models which best match the frame data.