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

Author:

De Almeida Alex Marino Gonçalves,Recco Claudineia Helena,Guido Rodrigo Capobianco

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.

Publisher

University of Florida George A Smathers Libraries

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimal 2D audio features estimation for a lightweight application in mosquitoes species: Ecoacoustics detection and classification purposes;Computers in Biology and Medicine;2024-01

2. Paraconsistent Feature Analysis for the Competency Evaluation of Voice Impersonation;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16

3. Statistical Analysis of Speech Disorder Specific Features to Characterise Dysarthria Severity Level;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

4. Knowledge-Based Wavelet Filters Prominently Detect Spoofed Speech;2021 IEEE International Symposium on Multimedia (ISM);2021-11

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