This paper presents an experimental comparison of different features for the detection of replay spoofing attacks in Automatic Speaker Verification systems. We evaluate the proposed countermeasures using two recently introduced databases, including the dataset provided for the ASVspoof 2017 challenge. This challenge provides researchers with a common framework for the evaluation of replay attack detection systems, with a particular focus on the generalization to new, unknown conditions (for instance, replay devices different from those used during system training). Our cross-database experiments show that, although achieving this level of generalization is indeed a challenging task, it is possible to train classifiers that exhibit stable and consistent results across different experiments. The proposed approach for the ASVspoof 2017 challenge consists in the score-level fusion of several base classifiers using logistic regression. These base classifiers are 2-class Gaussian Mixture Models (GMMs) representing genuine and spoofed speech respectively. Our best system achieves an Equal Error Rate of 10.52% on the challenge evaluation set. As a result of this set of experiments, we provide some general conclusions regarding feature extraction for replay attack detection and identify which features show the most promising results.
The COVID-19 pandemic has led to the saturation of public health services worldwide. In this scenario, the early diagnosis of SARS-Cov-2 infections can help to stop or slow the spread of the virus and to manage the demand upon health services. This is especially important when resources are also being stretched by heightened demand linked to other seasonal diseases, such as the flu. In this context, the organisers of the DiCOVA 2021 challenge have collected a database with the aim of diagnosing COVID-19 through the use of coughing audio samples. This work presents the details of the automatic system for COVID-19 detection from cough recordings presented by team PANACEA. This team consists of researchers from two European academic institutions and one company: EURECOM (France), University of Granada (Spain), and Biometric Vox S.L. (Spain). We developed several systems based on established signal processing and machine learning methods. Our best system employs a Teager energy operator cepstral coefficients (TECCs) based frontend and Light gradient boosting machine (LightGBM) backend. The AUC obtained by this system on the test set is 76.31% which corresponds to a 10% improvement over the official baseline.
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