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

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2 Author(s)
Rudasi, L. ; Dept. of Electr. & Comput. Eng., Old Dominion Univ., Norfolk, VA, USA ; Zahorian, Stephen A.

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

Published in:

Neural Networks, 1992. IJCNN., International Joint Conference on  (Volume:4 )

Date of Conference:

7-11 Jun 1992

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