Advanced Signal Analysis in Detecting Replay Attacks for Automatic Speaker Verification Systems
원문 초록
This study proposes novel signal analysis methods for replay speech detection in automatic speaker verification (ASV) systems. The proposed methods -- arbitrary analysis (AA), mel scale analysis (MA), and constant Q analysis (CQA) -- are inspired by the calculation of the Fourier inversion formula. These methods introduce new perspectives in signal analysis for replay speech detection by employing alternative sinusoidal sequence groups. The efficacy of the proposed methods is examined on the ASVspoof 2019 \& 2021 PA databases with experiments, and confirmed by the performance of systems that incorporated the proposed methods; the successful integration of the proposed methods and a speech feature that calculates temporal autocorrelation of speech (TAC) from complex spectra strongly confirms it. Moreover, the proposed CQA and MA methods show their superiority to the conventional methods on efficiency (approximately 2.36 times as fast compared to the conventional constant Q transform (CQT) method) and efficacy, respectively, in analyzing speech signals, making them promising to utilize in music and speech processing works.
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Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models
ASVspoof2019 vs. ASVspoof5: Assessment and Comparison
Pushing the Frontiers of Self-Distillation Prototypes Network with Dimension Regularization and Score Normalization
SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling
토론
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