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Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification

Song, Siyang; Zhang, Shuimei; Schuller, Björn W.; Shen, Linlin; Valstar, Michel

Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification Thumbnail


Authors

Siyang Song

Shuimei Zhang

Björn W. Schuller

Linlin Shen

Michel Valstar



Abstract

The performance of speaker-related systems usually degrades heavily in practical applications largely due to the presence of background noise. To improve the robustness of such systems in unknown noisy environments, this paper proposes a simple pre-processing method called Noise Invariant Frame Selection (NIFS). Based on several noisy constraints, it selects noise invariant frames from utterances to represent speakers. Experiments conducted on the TIMIT database showed that the NIFS can significantly improve the performance of Vector Quantization (VQ), Gaussian Mixture Model-Universal Background Model (GMM-UBM) and i-vector-based speaker verification systems in different unknown noisy environments with different SNRs, in comparison to their baselines. Meanwhile, the proposed NIFS-based speaker verification systems achieves similar performance when we change the constraints (hyper-parameters) or features, which indicates that it is robust and easy to reproduce. Since NIFS is designed as a general algorithm, it could be further applied to other similar tasks.

Conference Name International Joint Conference on Neural Networks 2018
Conference Location Rio de Janeiro, Brazil
Start Date Jul 8, 2018
End Date Jul 13, 2018
Acceptance Date Mar 15, 2018
Online Publication Date Oct 14, 2018
Publication Date 2018
Deposit Date Apr 30, 2018
Publicly Available Date Oct 14, 2018
Peer Reviewed Peer Reviewed
Series ISSN 2161-4407
Book Title Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN)
ISBN 978-1-5090-6015-3
DOI https://doi.org/10.1109/IJCNN.2018.8489497
Public URL https://nottingham-repository.worktribe.com/output/945806
Publisher URL https://ieeexplore.ieee.org/document/8489497
Related Public URLs http://www.ecomp.poli.br/~wcci2018/
Additional Information © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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