Making speaker verification (SV) systems robust to spoofed/mimicked speech attacks is very important to make its use effective in security applications. In this work, we show that using a proximal support vector machine backend classifier with i-vectors as inputs (i-PSVM) can help improve the performance of SV systems for mimicked speech as non-target trials. We compared our results with the state-of-the-art baseline i-vector with cosine distance scoring (i-CDS), i-vector with a backend SVM classifier (i-SVM) and cosine distance features with an SVM backend classifier (CDF-SVM) systems. In iPSVM, proximity of the test utterance to the target and nontarget class is the criteria for decision making while in i-SVM, the distance from the separating hyperplane is the criteria for the decision. It was seen that the i-PSVM approach is advantageous when tested with mimicked speech as non-target trials. This highlights that proximity to the target speakers is a better criteria for speaker verification for mimicked speech. Further, we note that weighting the target and non-target class examples helps us further fine tune the performance of i-PSVM. We then devised a strategy for estimating the weights for every example based on its cosine distance similarity with respect to the centroid of target class examples. The final i-PSVM with example based weighting scheme achieved an improvement of 3.39% absolute in EER when compared to the best baseline system, iSVM. Subsequently, we fused the i-PSVM and i-SVM systems and results show that the performance of the combined system is better than the individual systems.
K. K. George, Dr. Santhosh Kumar C., Dr. K. I. Ramachandran, and Panda, A., “Improving Robustness of Speaker Verification Against Mimicked Speech”, in Odyssey 2016, 2016.