Use of Paraconsistent Feature Engineering to support the Long Term Feature choice for Speaker Verification

Authors

  • Alex Marino Gonçalves de Almeida UNESP
  • Claudineia Helena Recco
  • Rodrigo Capobianco Guido

DOI:

https://doi.org/10.32473/flairs.v34i1.128370

Keywords:

Feature Engineering, Wavelet, Speaker Verification, Paraconsistent Logic

Abstract

The state-of-art models for speech synthesis and voice conversion can generate synthetic speech perceptually indistinguishable from human speech, and speaker verification is crucial to prevent breaches. The building feature that best distinguishes genuine speech between spoof attacks is an open research subject. We used the baseline ASVSpoof2017, Transfer Learning (TL) set, and Symlet and Daubechies Discrete Wavelet Packet Transform (DWPT) for this investigation. To qualitatively assess the features, we used Paraconsistent Feature Engineering (PFE). Our experiments pointed out that for the use of more robust classifiers, the best choice would be the AlexNet method, while in terms of classification regarding the Equal Error Rate metric, the best suggestion would be Daubechies filter support 21. Finally, our findings indicate that Symlet filter support 17 as the most promising feature, which is evidence that PFE is a useful tool and contributes to feature selection.

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Published

2021-04-18

How to Cite

de Almeida, A. M. G., Recco, C. H., & Guido, R. C. (2021). Use of Paraconsistent Feature Engineering to support the Long Term Feature choice for Speaker Verification. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128370

Issue

Section

Main Track Proceedings