17. The method of claim 16, wherein computing up-dated P.sub.n+1 (F.sub.2 (l).vertline.A.sub.ij) values comprises the steps of: (m) computing a relational count as: ##EQU26## (n) summing the relational counts over all transitions A.sub.ij as: ##EQU27## (o) from the summed relational counts, estimating up-dated confusion matrix values as: ##EQU28##
16. The method of claim 15, wherein said confusion matrix value computing step includes the steps of: (f) generating a short string of labels in the acoustic processor in response to the utterance by the subsequent speaker of a shortened sample text; (g) initializing transition probability values P.sub.n (A.sub.ij) and label probability values P.sub.n (F.sub.2 (l).vertline.A.sub.ij) for the phone machines corresponding to the subsequent speaker, based on the short string of labels, where index value n=0; (h) computing, with a forward-backward algorithm, up-dated transition counts count.sub.n+1 (A.sub.ij); (j) computing, with a forward-backward algorithm, up-dated label output probability counts as: ##EQU25## wherein .alpha..sub.t is a cumulative probability in moving forward to state i from an initial state and where .beta..sub.t+1 is a cumulative probability of proceeding backward from a final state of a Markov model sequence of states to state j; (k) from counts count.sub.n+1 (A.sub.ij) and counts count.sub.n+1 (F.sub.2 (l),A.sub.ij), computing up-dated transition probability values P.sub.n+1 (A.sub.ij) and up-dated values for P.sub.n+1 (F.sub.2 (l).vertline.A.sub.ij).