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Neural Computing and Applications
Published in: Neural Computing and Applications 17/2020

10-03-2020 | Original Article

Robust features for text-independent speaker recognition with short utterances

Authors: Rania Chakroun, Mondher Frikha

Published in: Neural Computing and Applications | Issue 17/2020

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Abstract

Speaker recognition systems achieve good performance under controlled conditions. However, in real-world conditions, the performance degrades drastically. The principal cause being when limited data are presented. The presence of background noise is another main factor of performance distortion. In spite of the major advances in speaker recognition field, the effect of noise and the limitation of the amount of available speech data are still open problems, and no optimal solution has been found yet to cope with them. In this paper, we propose a new system using new enhanced and reduced gammatone coefficients in order to improve robustness with limited speech data duration. We demonstrate the usefulness of these coefficients compared to the well-known features with speakers taken from different databases recorded under different conditions.
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Metadata
Title
Robust features for text-independent speaker recognition with short utterances
Authors
Rania Chakroun
Mondher Frikha
Publication date
10-03-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 17/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04793-y

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