10. In a speech recognizer which generates a string of label outputs in response to an uttered speech input and in which words are represented by Markov models characterized by a plurality of states and transitions between states, wherein the probability of each transition A.sub.ij from a state i to a state j and each respective probability of producing a given label output at a given transition differ between a reference speaker and a subsequent speaker, a computerized method of determining label output probabilities for the subsequent speaker comprising the steps of: (a) initializing transition probabilities P.sub.n (A.sub.ij), label output probabilities P.sub.n (F.sub.2 (l).vertline.A.sub.ij), and confusion matrix entries P.sub.n (F.sub.2 (l).vertline.F.sub.1 (k)) which indicate the similarity between the lth label output as spoken by the subsequent speaker and the kth label output as spoken by the reference speaker where n is initialized to zero; (b) applying a forward-backward algorithm to the values P.sub.n (A.sub.ij), P.sub.n (F.sub.2 (l).vertline.A.sub.ij), and P.sub.n (F.sub.2 (l).vertline.F.sub.1 (k)) to provide label output counts for the subsequent speaker count.sub.n+1 (F.sub.2 (l), A.sub.ij); (c) computing re-parameterized counts count.sub.n+1 (F.sub.2 (l), F.sub.1 (k), A.sub.ij) for each A.sub.ij transition; (d) computing updated probability values P.sub.n+1 (F.sub.2 (l).vertline.F.sub.1 (k)) for confusion matrix entries from the computed re-parameterized counts; (e) computing re-parameterized label output probabilities from the P(F.sub.1 (k).vertline.A.sub.ij) and P.sub.n+1 (F.sub.2 (l).vertline.F.sub.1 (k)) probability values; (f) incrementing n and repeating steps (b) through (e) until n equals a prescribed value nmax; the re-parameterized label output probabilities at iteration n=nmax representing updated re-parameterized label output probabilities; (g) linear averaging the updated re-parameterized label output probabilities against the initial label output probabilities to provide smoothed label output probabilities; (h) initializing the basic transition probabilities and basic label output probabilities; (j) applying the forward-backward algorithm to the basic probabilities; (k) repeating step (j) over a prescribed number of mmax of iterations to provide updated basic label output probabilities and transition probabilities; and (l) linear averaging the updated basic label output probabilities against the smoothed label output probabilities to provide final label output probabilities.
10. In a speech recognizer which generates a string of label outputs in response to an uttered speech input and in which words are represented by Markov models characterized by a plurality of states and transitions between states, wherein the probability of each transition A.sub.ij from a state i to a state j and each respective probability of producing a given label output at a given transition differ between a reference speaker and a subsequent speaker, a computerized method of determining label output probabilities for the subsequent speaker comprising the steps of: (a) initializing transition probabilities P.sub.n (A.sub.ij), label output probabilities P.sub.n (F.sub.2 (l).vertline.A.sub.ij), and confusion matrix entries P.sub.n (F.sub.2 (l).vertline.F.sub.1 (k)) which indicate the similarity between the lth label output as spoken by the subsequent speaker and the kth label output as spoken by the reference speaker where n is initialized to zero; (b) applying a forward-backward algorithm to the values P.sub.n (A.sub.ij), P.sub.n (F.sub.2 (l).vertline.A.sub.ij), and P.sub.n (F.sub.2 (l).vertline.F.sub.1 (k)) to provide label output counts for the subsequent speaker count.sub.n+1 (F.sub.2 (l), A.sub.ij); (c) computing re-parameterized counts count.sub.n+1 (F.sub.2 (l), F.sub.1 (k), A.sub.ij) for each A.sub.ij transition; (d) computing updated probability values P.sub.n+1 (F.sub.2 (l).vertline.F.sub.1 (k)) for confusion matrix entries from the computed re-parameterized counts; (e) computing re-parameterized label output probabilities from the P(F.sub.1 (k).vertline.A.sub.ij) and P.sub.n+1 (F.sub.2 (l).vertline.F.sub.1 (k)) probability values; (f) incrementing n and repeating steps (b) through (e) until n equals a prescribed value nmax; the re-parameterized label output probabilities at iteration n=nmax representing updated re-parameterized label output probabilities; (g) linear averaging the updated re-parameterized label output probabilities against the initial label output probabilities to provide smoothed label output probabilities; (h) initializing the basic transition probabilities and basic label output probabilities; (j) applying the forward-backward algorithm to the basic probabilities; (k) repeating step (j) over a prescribed number of mmax of iterations to provide updated basic label output probabilities and transition probabilities; and (l) linear averaging the updated basic label output probabilities against the smoothed label output probabilities to provide final label output probabilities. 13. A method as in claim 10 wherein step (l) includes the steps of: (r) dividing into two portions the labels generated by an acoustic processor in response to the subsequent speaker uttering part of a sample text; (s) entering the first portion of labels and updated basic probabilities into a forward-backward algorithm processor and computing counts C*.sub.1 and a probability P*.sub.1 ; (t) entering the second portion of labels and updated basic probabilities into a forward-backward algorithm processor and computing counts C*.sub.2 and a probability P*.sub.2 ; and (u) computing final label output probabilities according to the expression: ##EQU22## wherein .lambda.*.sub.m is a selectable weighting factor and wherein A.sub.ij represents a transition from a state i to a state j in a phone machine.