Text-independent speaker identification using binary-pair partitioned neural networks

The N-way speaker identification task is partitioned into N*(N-1)/2 binary-pair classifications. The binary-pair classifications are performed with small neural nets, each trained to make independent binary decisions on small fragments of speech data. Three issues were investigated concerning optimally combining a large number of fragmentary binary decisions into a single N-way decision: (1) incorporating speech energy and phonetic content information to compute an improved probability measure at the individual speech frame level; (2) combining binary frame-level decisions into a binary segment-level decision; and (3) combining the binary segment-level decisions into a single N-way segment level decision. It was shown that the two-way classifiers can be combined to achieve 100% speaker identification performance for large speaker populations