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Constructing a dataset for replay spoofing detection requires a physicalprocess of playing an utterance and re-recording it, presenting a challenge tothe collection of large-scale datasets. In this study, we propose aself-supervised framework for pretraining acoustic configurations usingdatasets published for other tasks, such as speaker verification. Here,acoustic configurations refer to the environmental factors generated during theprocess of voice recording but not the voice itself, including microphonetypes, place and ambient noise levels. Specifically, we select pairs ofsegments from utterances and train deep neural networks to determine whetherthe acoustic configurations of the two segments are identical. We validate theeffectiveness of the proposed method based on the ASVspoof 2019 physical accessdataset utilizing two well-performing systems. The experimental resultsdemonstrate that the proposed method outperforms the baseline approach by 30%.

Self-supervised pre-training with acoustic configurations for replay spoofing detection