Reusable binary-paired partitioned neural networks for text-independent speaker identification

A neural network algorithm for speaker identification with large groups of speakers is described. This technique is derived from a technique in which an N-way speaker identification task is partitioned into N*(N-1)/2 two-way classification tasks. Each two-way classification task is performed using a small neural network which is a two-way, or pair-wise, network. The decisions of these two-way networks are then combined to make the N-way speaker identification decision (Rudasi and Zahorian, 1991 and 1992). Although very accurate, this method has the drawback of requiring a very large number of pair-wise networks. In the new approach, two-way neural network classifiers, each of which is trained only to separate two speakers, are also used to separate other pairs of speakers. This method is able to greatly reduce the number of pair-wise classifiers required for making an N-way classification decision, especially when the number of speakers is very large. For 100 speakers extracted from the TIMIT database, the number of pair-wise classifiers can be reduced by approximately a factor of 5, with only minor degradation in performance when 3 seconds or more of speech is used for identification. Using all 630 speakers from the TIMIT database, this method can be used to obtain over 99.7% accuracy. With the telephone version of the same database, an accuracy of 40.2% can be obtained.