Text-independent talker identification with neural networks

Publisher Website
Google Scholar
Abstract
The authors introduce a novel method for partitioning a large classification problem using N*(N-1)/2 binary pair classifiers. The binary pair classifier has been applied to a speaker identification problem using neural networks for the binary classifiers. The binary partitioned approach was used to develop an identification system for the 47 male speakers belonging to the Northern dialect region of the TIMIT database. The system performs with 100% accuracy in a text-independent mode when trained with about 9 to 14 s of speech and tested with 8 s of speech. The partitioned approach performs comparably, or even better, than a single large neural network. For large values of N (>10), the partitioned approach requires only a fraction of the training time required for a single large network. For N=47, the training time for the partitioned network would be about two orders of magnitude less than for the single large network.

This publication has 2 references indexed in Scilit: