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Proceedings Paper

Psychoacoustic speech feature optimization through adaptive generalized scale transforms
Author(s): Robert M. Nickel
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Paper Abstract

We are presenting a method for the improvement of small scale text independent automatic speaker identification systems. A small scale identification system is a system with a relatively small number of enrolled speakers (20 or less). The proposed improvement is obtained from adaptive frequency warping. Most modern speaker identification systems employ a short-time speech feature extraction method that relies on frequency warped cepstral representations. One of the most popular frequency warping types is based on the mel-scale. While the mel-scale provides a substantial boost in recognition performance for large scale systems, it is suboptimal for small scale systems. With experiments we have shown that our methodology has the potential to reduce the error rate of small scale systems by 24% over the mel-scale approach.

Paper Details

Date Published: 3 September 2008
PDF: 8 pages
Proc. SPIE 7074, Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII, 70740U (3 September 2008); doi: 10.1117/12.795465
Show Author Affiliations
Robert M. Nickel, Bucknell Univ. (United States)

Published in SPIE Proceedings Vol. 7074:
Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII
Franklin T. Luk, Editor(s)

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