1. In a Markov model speech recognition system having an acoustic processor which generates a string of labels in response to an uttered input where each label is one of an alphabet of labels, a computerized method of constructing Markov model word baseforms comprising the steps of: (a) for each of a set of Markov models in which each Markov model corresponds to a respective label and in which each Markov model has (i) a plurality of states and (ii) a plurality of arcs wherein each arc extends from a state to a state, computing and storing in computer memory arc probabilities and label output probabilities wherein each label output probability represents the likelihood of a given label being produced at a given arc; (b) generating, with the acoustic processor, n respective strings of labels in response to each of n utterances of a subject word selected from a vocabulary of words; (c) selecting the string of labels having a length which is closest to the average length of all strings generated in step (b); (d) concatenating in sequence the Markov models which correspond to the successive labels in the selected string and storing the concatenated sequence; (e) for a string other than the selected string, aligning successive substrings of zero or more labels against successive Markov models in the concatenated sequence, based on the stored probabilities; (f) repeating step (e) for each generated string of step (b) other than the selected string, each string generated in step (b) having a respective substring corresponding to each Markov model in the concatenated sequence of step (d); (g) partitioning the generated strings of step (b) into successive common segments, the ith common segment of each string corresponding to the i th substring thereof; and (h) constructing a sequence of one or more Markov models for each ith common segment based on the ith label of the prototype string and the ith substrings of the other strings.