Performance Analysis of Machine Learning Techniques for Voice Spoofing Detection

Voice-based authentication is widely used today but remains vulnerable to spoofing attacks like text-to-speech and voice conversion, threatening privacy and its authenticity. To mitigate such threats, various detection techniques ranging from conventional machine learning models to contemporary deep learning frameworks have been used in the past. Yet, it is difficult to choose an ideal solution, as model performance typically varies with dataset attributes, computational power, and deployment environments.This paper is a comparative experimental analysis of four spoofing detection models as controllers: Support Vector Machine (SVM), Gaussian Mixture Model (GMM), ResNet, and Wav2Vec 2.0 with any transformer. The models are tested with one experiment through the usage of the automatic speaker verification spoof 2019 Logical Access dataset. Equal error rate and tandem detection cost function measures the effectiveness of the detection, whereas training and inference time measure the effectiveness of the computation. Results, under the standardized set up, are that ResNet is typified with a good detection, at a comparatively low computational cost, and Wav2Vec 2.0 gives good error rates, at a significantly larger cost of fine-tuning. The GMM and classical SVM models are lower in their cost of computation and greater in error rates, respectively.Overall, the results reveal that more complex models do not always improve, and using the detection performance and runtime analysis, the study gives appropriate guidance on how to choose the spoof detection models based on the deployment needs, not only the accuracy.