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Exploration of multi-source discriminative acoustic feature for speaker recognition with short-duration audio signal

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Abstract

This work aims to further compensate for the weaknesses of feature sparsity and insufficient discriminative acoustic features in existing short-duration audio voiceprint recognition. To address this issue, we propose a Bark-scaled Gaussian and linear filter bank superposition cepstral coefficients (BGLCC), a multi-dimensional central difference (MDCD) acoustic feature, and a Fisher feature fusion of multi-source features. First, the rich low-frequency information is extracted based on the high distribution density of the Bark-scaled Gaussian filter bank in the low-frequency domain, and the extraction of more high-frequency information is based on the linear filter bank uniformly distributed in the high-frequency domain. In addition, the multidimensional central difference method captures better dynamic features of voiceprints in the relative BGLCC domain to improve the performance of short utterance speaker recognition. Finally, the Fisher feature fusion method (FFF) can further enhance speaker individual information and reduce speaker commonality information. Extensive experiments are conducted on short-duration text-independent speaker verification datasets generated from the VoxCeleb corpus, which speech samples of diverse lengths. The results demonstrate that the proposed method outperforms the existing acoustic feature extraction approach by at least 15% in the test set. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods.

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All data generated or analyzed during this study are included in this published article [15].

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Acknowledgements

This work was in part supported by NSFC [Grant No. 62176194, Grant No.62101393], the Major project of IoV [Grant No. 2020AAA001], Sanya Science and Education Innovation Park of Wuhan University of Technology [Grant No. 2021KF0031], CSTC [Grant No. cstc2021jcyj-msxmX1148], and the Open Project of Wuhan University of Technology Chongqing Research Institute [ZL2021–6].

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  1. School of Computer and Artificial Intelligence, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, China

    Yunfei Zi & Shengwu Xiong

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  1. Yunfei Zi
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Zi, Y., Xiong, S. Exploration of multi-source discriminative acoustic feature for speaker recognition with short-duration audio signal. Multimed Tools Appl 82, 47537–47557 (2023). https://doi.org/10.1007/s11042-023-16378-7

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