1. In a speech recognizer in which each successive interval of speech is associated with one label output of an alphabet of label outputs and in which words correspond to respective sequences of Markov model phone machines, wherein each phone machine has a plurality of transitions each extending from a state i to a state j and wherein the probability of each transition and the probability of label outputs being produced at transitions differ among speakers, computerized apparatus for training the recognizer to the probabilities of a subsequent speaker after the recognizer is trained with the probabilities of a reference speaker in response to the utterance by said reference speaker of a sample text, the apparatus comprising: acoustic processor means for outputting a string of one label after another at successive time intervals in response to a speech input; means for computing basic label output probability values from labels generated by said acoustic processor means in response to the subsequent speaker uttering part of the sample text; means for generating smoothed label output probabilities for the subsequent speaker which are based on a similarity measure determined between the subsequent speaker and the reference speaker, the smoothed label output probabilities being more informative than the basic label output probabilities; and means for linear averaging the basic label output probabilities against the smoothed label output probabilities, given the label outputs generated in response to the subsequent speaker uttering said part of the sample text, to produce final label output probabilities.
1. In a speech recognizer in which each successive interval of speech is associated with one label output of an alphabet of label outputs and in which words correspond to respective sequences of Markov model phone machines, wherein each phone machine has a plurality of transitions each extending from a state i to a state j and wherein the probability of each transition and the probability of label outputs being produced at transitions differ among speakers, computerized apparatus for training the recognizer to the probabilities of a subsequent speaker after the recognizer is trained with the probabilities of a reference speaker in response to the utterance by said reference speaker of a sample text, the apparatus comprising: acoustic processor means for outputting a string of one label after another at successive time intervals in response to a speech input; means for computing basic label output probability values from labels generated by said acoustic processor means in response to the subsequent speaker uttering part of the sample text; means for generating smoothed label output probabilities for the subsequent speaker which are based on a similarity measure determined between the subsequent speaker and the reference speaker, the smoothed label output probabilities being more informative than the basic label output probabilities; and means for linear averaging the basic label output probabilities against the smoothed label output probabilities, given the label outputs generated in response to the subsequent speaker uttering said part of the sample text, to produce final label output probabilities. 5. The apparatus of claim 1 wherein said smoothed label generating means includes: re-parameterizing means for computing smoothed label output probabilities P.sub.s (F.sub.2 (l).vertline.A.sub.ij) in response to receiving as inputs (i) the known label output probabilities of the reference speaker P(F.sub.1 (k).vertline.A.sub.ij), (ii) initialized label output probabilities and transition probabilities P.sub.o (F.sub.2 (l).vertline.A.sub.ij) and P.sub.o (A.sub.ij), respectively, and (iii) labels generated by said acoustic processor means in response to the subsequent speaker uttering said part of the sample text.