Comparison of clustering methods for MLP-based speaker verification

This paper compares two clustering methods: SOM, and a graph-based clustering technique , for text-independent speaker verification. The focus of comparison is given to the distribution characteristics of representative frames for each cluster, to the use of processing time of clustering and MLP learning, and to verification performance. Simulation results show that the graph-based technique produces better verification performance than SOM. Other statistics are collected to explain significant difference in MLP learning time with each clustering method. This experiment suggests that there is a best match between a classifier and a clustering method for a given application.