Abstract
The popularity and portability of high-fidelity recording devices and playback devices pose severe challenges for speaker recognition systems against replay voice attacks. In this paper, the signal of audio is transformed into the frequency domain through the Fourier trans-form and constant Q transform. Compared with genuine voice, the mean and standard deviation of the replay voice at each frequency bin has changed slightly. And through the coefficient of variation to further analyze the difference between genuine voice and replay voice. A detection algorithm based on fusion feature is proposed. The algorithm uses two kinds of time-frequency transform coefficients and their cepstrum characteristics to train the GMM model and calculate the likelihood ratio score. Finally, the replay voice is detected by the fusion of scores. The experimental results show that the algorithm is about 13% lower than the baseline EER provided by The ASV Spoof 2017.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. U1736215, 61672302), Zhejiang Natural Science Foundation (Grant No. LZ15F020002, LY17F020010), Ningbo Natural Science Foundation (Grant No. 2017A610123), Ningbo University Fund (Grant No. XKXL1509, XKXL1503).
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Lin, L., Wang, R., Yan, D., Li, C. (2018). A Replay Voice Detection Algorithm Based on Multi-feature Fusion. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_27
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DOI: https://doi.org/10.1007/978-3-030-00021-9_27
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