Data augmentation based novel approach to automatic speaker verification system

Abstract

In recent times, the Automatic Speaker Verification (ASV) systems have been gaining popularity among biometric systems. Feature extraction technique, classification model, and spoof dataset are the three main components that mainly affect the performance of such systems. In recent years, researchers have proposed integrated or complex feature extraction methods at front end and combination of two or more machine learning models at backend to classify the audio samples into spoofed or bonafide. This increases the overall complexity of ASV system. Moreover, most of the spoofing datasets are highly imbalanced. Hence, to solve these issues, this paper improves front-end feature extraction by combining data augmentation approach Synthetic Minority Oversampling Technique (SMOTE) with less complex feature extraction method Frequency Domain Linear Prediction (FDLP). In the proposed system, classification of the audio samples into bonafide or spoofed samples has been done by using different backend classification models such as Random Forest (RF), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Convolutional Neural Network (CNN), Long Short-Term Memory, and Bidirectional LSTM (BiLSTM). The proposed system has been built using ASVspoof PA 2019, ASVspoof LA 2019 and VSDC datasets, and has been evaluated on Logical Access (LA) attacks, Presentation attacks (PA) and multi-order replay attacks (MA). The obtained results show that combination of SMOTE oversampling with FDLP at frontend, and BiLSTM at backend outperforms all other implemented models. It provides Equal Error Rate (EER) value 0.85 %, 0.91 % and 0.55 % for PA, LA and MA attacks scenarios respectively. The performance of the proposed system has also been evaluated in the presence Gaussian noise. It can be interpreted from the obtained results that proposed FDLP-SMOTE-BiLSTM system provides better performance in noisy environment, and under different spoofing attacks scenarios.