Speaker-Specific Method of Spoofing Attack Detection Based on Anomaly Detection

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Most research in the field of voice presentation attack detection relies on the speaker-independent approach. Nevertheless, several scientific works indicate that using the speaker-specific approach, which involves utilizing prior knowledge about the identity of the claimed speaker to enhance the accuracy of spoofing detection, is likely to be beneficial. Therefore, the goal of this work is to propose a speaker-specific method of spoofing attack detection based on anomaly detection and to evaluate its applicability to the detection of synthesized speech and converted voice. Artificial neural networks pre-trained for the tasks of spoofing detection, speaker recognition, and audio pattern recognition are used for feature extraction. A set of anomaly detection models are used as backend classifiers. Each of them is trained on bonafide data of a target speaker. The experimental evaluation of the proposed method on the ASVspoof 2019 LA dataset shows that the best speaker-specific spoofing detection system, which uses an anomaly detection model and a neural network pre-trained for the task of speaker recognition, achieves an EER of 4.74%. This result suggests that embeddings extracted by networks pre-trained for speaker recognition contain information that can be utilized for spoofing detection. In addition, the proposed method allowed to increase the accuracy of three baseline systems pre-trained for the task of spoofing detection. Experiments with two baseline systems on the ASVspoof 2019 LA dataset showed relative improvement in terms of EER by 7.1% and 9.2%, and in terms of min t-DCF by 4.6%. Experiments with the third baseline system on the ASVspoof 2021 LA dataset showed relative improvement in terms of EER by 3.9% without significant improvement of min t-DCF.