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Feature selection via dynamic programming for text-independent speaker identification

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2 Author(s)
Cheung, R. ; GTE Sylvania Inc., Needham Heights, MA ; Eisenstein, B.

Dynamic programming is applied to the selection of feature subsets in text-independent speaker identification. Each feature is long-term averaged in order to reduce its variability to text information. The resulting subset of features shows a lower average identification error in comparison to that of the "knock-out" strategy, the cepstral coefficients, and the PARCOR coefficients.

Published in:

Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:26 ,  Issue: 5 )