Publication Type:

Conference Paper

Source:

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2016)

ISBN:

9781509020294

URL:

http://ieeexplore.ieee.org/abstract/document/7732417/

Keywords:

ASV systems, automatic speaker verification systems, cepstral analysis, CNPCC, cosine normalised phase-based cepstral coefficients, Databases, Feature extraction, Gaussian mixture model back-end classifier, Gaussian processes, GMM back-end classifier, Mel frequency cepstral coefficient, mixture models, Robust features, score level fusion, Security of data, signal classification, Speaker recognition, Speech, spoofing attacks, spoofing detection, system attacks, Training, unknown attacks

Abstract:

It is very important to enhance the robustness of Automatic Speaker Verification (ASV) systems against spoofing attacks. One of the recent research efforts in this direction is to derive features that are robust against spoofed speech. In this work, we experiment with the use of Cosine Normalised Phase-based Cepstral Coefficients (CNPCC) as inputs to a Gaussian Mixture Model (GMM) back-end classifier and compare its results with systems developed using the popular short term cepstral features, Mel-Frequency Cepstral Coefficients (MFCC) and Power Normalised Cepstral Coefficients (PNCC), and show that CNPCC outperforms the other features. We then perform a score level fusion of the system developed using CNPCC with that of the systems using MFCC and PNCC to further enhance the performance. We use known attacks to train and optimise the system and unknown attacks to evaluate and present the results.

Cite this Research Publication

A. Sathya, Swetha, J., Das, K. A., George, K. K., Dr. Santhosh Kumar C., and Aravinth J., “Robust features for spoofing detection”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016.

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