Considerations in applying clustering techniques to speaker independent word recognition

Recent work at Bell Laboratories has demonstrated the utility of applying sophisticated pattern recognition techniques to obtain a set of speaker independent word templates for an isolated word recognition system [1,2]. In these studies, it was shown that a careful experimenter could guide the clustering algorithms to choose a small set of templates that were representative of a large number of replications for each word in the vocabulary. Subsequent word recognition tests verified that the templates chosen were indeed representative of a fairly large population of talkers. Given the success of this approach, the next important step is to investigate fully automatic techniques for clustering multiple versions of a single word into a set of speaker independent word templates. Two such techniques are described in this paper. The first method uses distance data (between replications of a word) to segment the population into stable clusters. The word template is obtained as either the cluster minimax, or as an averaged version of all the elements in the cluster. The second method is a variation of the one described by Rabiner [3] in which averaging techniques are directly combined with the nearest neighbor rule to simultaneously define both the word template (i.e. the cluster center) and the elements in the cluster. Experimental data shows the first method to be superior to the second method when 3 or more clusters per word are used in the recognition task.