Abstract
The authenticated users are identified and verified using automatic speaker verification (ASV) technologies. An automatic speaker verification (ASV) system, like any other user identification system, is also sensitive to spoofing. In order to make the ASV systems robust against spoofing, these systems are alienated into two different phases, i.e., frontend feature extraction and backend classification model. The main emphasis of the paper is on the development of the system against multi-order replay attacks. The joint frequency-domain linear prediction (FDLP) and mel-frequency cepstral coefficients (MFCC) is used at frontend to extract the features from the audio samples. At backend, gated recurrent unit (GRU) classification model is used. The proposed system is achieving 2.99% equal error rate (ERR) and 1.6% ERR under 1PR and 2PR spoofing attacks, respectively, and also provides 97.7% and 97.9% accuracy under the same environment.
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Joshi, S., Dua, M. (2023). Multi-order Replay Attack Detection Using Enhanced Feature Extraction and Deep Learning Classification. In: Mahapatra, R.P., Peddoju, S.K., Roy, S., Parwekar, P. (eds) Proceedings of International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol 600. Springer, Singapore. https://doi.org/10.1007/978-981-19-8825-7_63
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DOI: https://doi.org/10.1007/978-981-19-8825-7_63
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