A redundant hash addressing method adapted for the postprocessing and error-correction of computer recognized speech

In the recognition of spoken words a frequently applied method is to first convert the acoustic waveforms into phonemic strings which are then compared with prototype strings stored in a dictionary, using some metric. A standard method is to use dynamic programming for comparison of strings with variable length. This procedure, however, is rather slow. A recently introduced principle of string comparison is based on redundant hash addressing, and it is computationally at least an order of magnitude lighter. This method is here applied using multiple prototypes of phonemic strings for each word in the dictionary. The matching criterium thereby applied in fact corresponds to a distance-weighted k-nearest-neighbor classifier which allows length variations in strings.