12. A method for recognizing an unknown utterance as one of a set of reference words according to claim 11 wherein said feature signal set generating step comprises producing prediction parameter signals representative of the utterance; and said similarity signal generating step comprises producing a signal representative of the distance between the unknown utterance prediction parameter signals and the reference word template prediction parameter signals jointly responsive to the prediction parameter signals of the unknown utterance and the prediction parameter signals of each reference word template characterized in that the smallest distance signals for each reference word are selected; a signal representative of the average of the selected distance signals for each reference word is formed; and responsive to the average distance signals formed for all reference words, the unknown utterance is identified as the reference word having the least average distance signal.
12. A method for recognizing an unknown utterance as one of a set of reference words according to claim 11 wherein said feature signal set generating step comprises producing prediction parameter signals representative of the utterance; and said similarity signal generating step comprises producing a signal representative of the distance between the unknown utterance prediction parameter signals and the reference word template prediction parameter signals jointly responsive to the prediction parameter signals of the unknown utterance and the prediction parameter signals of each reference word template characterized in that the smallest distance signals for each reference word are selected; a signal representative of the average of the selected distance signals for each reference word is formed; and responsive to the average distance signals formed for all reference words, the unknown utterance is identified as the reference word having the least average distance signal. 13. A method for recognizing an unknown utterance as one of a set of reference words according to claim 12 wherein said partitioning step is further characterized in that a set of signals corresponding to the distances between pairs of said reference word feature signal sets is generated and stored; responsive to the stored distance signals, the centermost set of the reference word feature signal sets is determined and a first group of said feature signal sets which are within a predetermined distance of said centermost set is identified; successive groups of unclustered feature signal sets having a predetermined degree of similarity are formed by determining the centermost set of the preceding group of feature signal sets from the stored distance signals and identifying the feature signal sets of said preceding group which are within the predetermined distance of said preceding group centermost set as members of the next successive group; responsive to all feature signal sets of a formed group being within the predetermined distance of the group centermost set, identifying the formed group as a cluster of feature signal sets having a predetermined degree of similarity.