An Improved Ranking-Based Feature Enhancement Approach for Robust Speaker Recognition

Yan, Furong; Men, Aidong; Yang, Bo; Jiang, Zhuqing

doi:10.1109/access.2016.2607778

Cited by 11 publications

(1 citation statement)

References 22 publications

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“…The proposed AN-BN features are applied to speaker verification (SV). As we know, the performance of classical SV systems, such as Gaussian Mixture Model-Universal Background Model (GMM-UBM) [18] and i-Vector systems [19], greatly degrades when speech signals are corrupted by additive noises [20]. Many works have been done on developing noise robust SV systems during last decades [21].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Network Bottleneck Features for Noise Robust Speaker Verification

Yu¹,

Guo

2017

Interspeech 2017

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In this paper, we propose a noise robust bottleneck feature representation which is generated by an adversarial network (AN). The AN includes two cascade connected networks, an encoding network (EN) and a discriminative network (DN). Melfrequency cepstral coefficients (MFCCs) of clean and noisy speech are used as input to the EN and the output of the EN is used as the noise robust feature. The EN and DN are trained in turn, namely, when training the DN, noise types are selected as the training labels and when training the EN, all labels are set as the same, i.e., the clean speech label, which aims to make the AN features invariant to noise and thus achieve noise robustness. We evaluate the performance of the proposed feature on a Gaussian Mixture Model-Universal Background Model based speaker verification system, and make comparison to MFCC features of speech enhanced by short-time spectral amplitude minimum mean square error (STSA-MMSE) and deep neural network-based speech enhancement (DNN-SE) methods. Experimental results on the RSR2015 database show that the proposed AN bottleneck feature (AN-BN) dramatically outperforms the STSA-MMSE and DNN-SE based MFCCs for different noise types and signal-to-noise ratios. Furthermore, the AN-BN feature is able to improve the speaker verification performance under the clean condition.

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