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Voice spoofing detection based on acoustic and glottal flow features using conventional machine learning techniques

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Abstract

Automatic Speaker Verification (ASV) systems are vulnerable to spoofing attacks. Most existing spoofing detection systems rely on two main points; the feature extraction and the classification methodology. In this paper, we propose a new strategy to recognize the veritable discourse from the spoofed one. The thought depends on the investigation of the human voice to identify the relevant acoustic and glottal features. Those features will be utilized to separate between a veritable discourse and a spoofed one. We have tested numerous of speech acoustic and glottal flow features from all data sets of ASVspoof challenge 2015 and ASVspoof challenge 2017. Several features are extracted and analyzed to choose the most pertinent ones using feature engineering methodology. To detect the genuine speech from the spoofed one, conventional machine learning techniques are applied as classification techniques mainly Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost). Features exploration and analysis leads to pick up pertinent ones. These features are used then as input for the SVM with multiple kernel and for XGBoost classification techniques. The highest rate of achieving accuracy is about 98.80% obtained with the XGBoost classification technique. Experimental results show the validity and the robustness of the proposed method.

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Authors and Affiliations

  1. National School of Engineers of Sfax (ENIS)/(MIRACL) Multimedia, InfoRmation systems and Advanced Computing Laboratory, University of Sfax, Sfax, Tunisia

    Raoudha Rahmeni

  2. Faculty of Sciences of Bizerte (FSB)/ (COSIM) Communication, Signals and IMages, University of Carthage, Carthage, Tunisia

    Anis Ben Aicha

  3. Higher Institute of Computer Sciences and Multimedia (ISIMS)/ (MIRACL) Multimedia, InfoRmation systems and Advanced Computing Laboratory, University of Sfax, Sfax, Tunisia

    Yassine Ben Ayed

Authors
  1. Raoudha Rahmeni
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  2. Anis Ben Aicha
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  3. Yassine Ben Ayed
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Correspondence to Raoudha Rahmeni.

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Rahmeni, R., Aicha, A.B. & Ayed, Y.B. Voice spoofing detection based on acoustic and glottal flow features using conventional machine learning techniques. Multimed Tools Appl 81, 31443–31467 (2022). https://doi.org/10.1007/s11042-022-12606-8

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  • DOI: https://doi.org/10.1007/s11042-022-12606-8

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