1. A method for assigning a label to a segment of speech to be recognized, comprising the steps of: providing a hierarchical fast ranking tree comprising a plurality of levels of subsets of prototypes, each prototype in a higher level subset being associated with one or more prototypes in a lower level subset; inputting a feature vector signal representing the segment of speech to be recognized; comparing the features of the vector signal with the features of the prototypes in a first level to find a first ranked list of the closest prototypes to the feature vector signal at that level; comparing the features of the feature vector signal to the prototypes in a second level subset associated with the highest ranking prototypes in the first ranked list of prototypes, to find a second ranked list of the closest prototypes to the feature vector signal in the second level; assigning the label associated with the highest ranking prototype in the lowest level subset to the feature vector signal; and predictive labeling wherein the highest ranking prototype in the lowest level subset is assigned to a second feature vector signal which represents another segment of speech to be recognized if a distance between the second feature vector signal and the first feature vector signal is at least less than a predetermined threshold.
1. A method for assigning a label to a segment of speech to be recognized, comprising the steps of: providing a hierarchical fast ranking tree comprising a plurality of levels of subsets of prototypes, each prototype in a higher level subset being associated with one or more prototypes in a lower level subset; inputting a feature vector signal representing the segment of speech to be recognized; comparing the features of the vector signal with the features of the prototypes in a first level to find a first ranked list of the closest prototypes to the feature vector signal at that level; comparing the features of the feature vector signal to the prototypes in a second level subset associated with the highest ranking prototypes in the first ranked list of prototypes, to find a second ranked list of the closest prototypes to the feature vector signal in the second level; assigning the label associated with the highest ranking prototype in the lowest level subset to the feature vector signal; and predictive labeling wherein the highest ranking prototype in the lowest level subset is assigned to a second feature vector signal which represents another segment of speech to be recognized if a distance between the second feature vector signal and the first feature vector signal is at least less than a predetermined threshold. 8. The method as claimed in claim 1, wherein each prototype includes a plurality of elements, each element being represented by a Gaussian density distribution including a mean value and a variance value.