Spoof speech classification using deep speaker embeddings and machine learning models
Mohammed Hamzah Abed, Dávid Sztahó · Array · 2025
This paper examines the effectiveness of deep speaker embeddings combined with machine learning classifiers for spoof speech detection. We leverage four state-of-the-art speaker embedding models: X-vector, Emphasized channel attention, propagation and aggregation in time delay neural network (ECAPA-TDNN), Residual Network - Time Delay Neural Network (ResNet-TDNN), and WavLM, used in both pre-trained and fine-tuned forms, to extract speaker-discriminative features from speech signals. These embeddings are used with five classifiers: Support Vector Machine, Random Forest, Multi-Layer Perceptron, Logistic regression, and XGBoost, to classify if a speech sample is a deepfake or not. We apply multiple feature scaling strategies and assess performance using standard metrics as well as the receiver operating characteristic (ROC) curve. Our results show that fine-tuned ECAPA-TDNN embeddings consistently outperform others across classifiers. This work contributes a robust pipeline for automated spoof speech classification, serving as a critical preprocessing step for other systems like forensic voice comparison.