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Proceedings Paper

Tactical speaker recognition using feature and classifier fusion
Author(s): Laurie H. Fenstermacher; Douglas Smith
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Paper Abstract

Tactical communications are inherently short and exhibit a great deal of channel variability. A novel speaker recognition technique was developed which uses on-line training to circumvent the need for excessive speaker or channel modeling. The technique incorporates both feature set fusion and classifier fusion. Separate classifiers are trained for each feature set: liftered LPC cepstra, RASTA liftered cepstra concomitant with delta cepstra. For each classifier, the results of the individual (feature) classifiers are adjudicated to rank the speakers. A final step adjudicates the results of different classifiers to determine the correct speaker.

Paper Details

Date Published: 2 March 1994
PDF: 8 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169995
Show Author Affiliations
Laurie H. Fenstermacher, Rome Lab. (United States)
Douglas Smith, Rome Lab. (United States)

Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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