ResNet152: A Deep Learning Approach for Robust Spoof Detection in Speaker Verification Systems

In human life, we know that sound is the most important factor. From the normal perspective to the intelligent perspective, sound develops automated systems for various fields for several purposes. However, within contemporary conventional systems, there is significant abuse leading to the proliferation of forgery and other crimes, with sound often playing a central role. With the help of the latest technology such as deep learning, there comes a vast possibility of integrating with many systems for boosting the efficiency of existing systems. So, in this paper, we bring an effective classification of audio using ResNet152. The audio signals are converted to spectrogram images and are passed to a classifier for generating binary classification such as genuine or spoof. We also evaluated our model with existing methods such as VGG16, CNN, VGG19, and AlexNet under performance measures such as Accuracy, EER, and t-DCF in which the proposed model outperforms with 92.2% testing accuracy and 82.2% inference accuracy.