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Generative and Discriminative Modelling of Linear Energy Sub-bands for Spoof Detection in Speaker Verification Systems

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Abstract

Classification of genuine and spoofed utterance is the basis for most of the countermeasure detecting spoof attacks on automatic speaker verification system. The choice of a good discriminating feature and a complementing classifier adds to the robustness of the countermeasure. Cepstral coefficients of the linear sub-band energy analysis have proved its worth in countering unknown attacks as witnessed by the literature. The intention behind the proposed work is to assess the behaviour of a spoof detection countermeasure using linear frequency cepstral coefficients with both generative and discriminative classifiers. The same are considered as baseline systems for further analysis. Parallelly, the paper proposes modifications to the traditional weighting function used in the retrieval of energy sub-bands on linear scale in order to leverage its full potential in spoof detection. The weighting function used is Gaussian, and hence, the modified feature is referred as GaussFCC. The aforementioned analysis is carried out on non-pre-emphasised utterances. The classifiers used are Gaussian mixture model (generative) and bidirectional long short-term memory (discriminative) classifiers. The empirical results show that the generative classifier has performed significantly in the detection of spoof attacks under logical access condition and discriminative classifier has shown drastic improvement in spoof detection under physical access condition over the generative model. Tandem detection cost function for logical access scenario (LA) using GMM classifier is 0.000 for development data and 0.113 for evaluation data, and in physical access scenario using BiLSTM classifier, it is 0.030 for development data and 0.044 for evaluation data. A detailed comparative analysis of the performance of the countermeasure is carried out based on different types of attacks, features, classifiers and utterances from female and male speakers.

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Acknowledgements

We extend our thanks to SSN College of Engineering for providing us with the required infrastructure to carry out our research work.

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  1. Department of CSE, SSN College of Engineering, Chennai, Tamil Nadu, India

    Suvidha Rupesh Kumar & B. Bharathi

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  1. Suvidha Rupesh Kumar
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Correspondence to Suvidha Rupesh Kumar.

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Rupesh Kumar, S., Bharathi, B. Generative and Discriminative Modelling of Linear Energy Sub-bands for Spoof Detection in Speaker Verification Systems. Circuits Syst Signal Process 41, 3811–3831 (2022). https://doi.org/10.1007/s00034-022-01957-0

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