Tandem spoofing-robust automatic speaker verification based on time-domain embeddings
Avishai Weizman, Yehuda Ben-Shimol, Itshak Lapidot

TL;DR
This paper introduces a novel time-domain embedding method based on the probability mass function of waveform amplitudes for spoofing-robust speaker verification, emphasizing gender-specific features and fusion with traditional systems to improve detection accuracy.
Contribution
The study presents a new time-domain embedding approach using PMF of waveform amplitudes, incorporating gender recognition and fusion techniques to enhance spoofing detection in speaker verification.
Findings
Gender recognition accuracy with mismatch rates of 0.94% (male) and 1.79% (female)
Equal error rates of 8.67% (male) and 10.12% (female) in gender-specific CM systems
Fusion with traditional systems improves generalization and detection performance.
Abstract
Spoofing-robust automatic speaker verification (SASV) systems are a crucial technology for the protection against spoofed speech. In this study, we focus on logical access attacks and introduce a novel approach to SASV tasks. A novel representation of genuine and spoofed speech is employed, based on the probability mass function (PMF) of waveform amplitudes in the time domain. This methodology generates novel time embeddings derived from the PMF of selected groups within the training set. This paper highlights the role of gender segregation and its positive impact on performance. We propose a countermeasure (CM) system that employs time-domain embeddings derived from the PMF of spoofed and genuine speech, as well as gender recognition based on male and female time-based embeddings. The method exhibits notable gender recognition capabilities, with mismatch rates of 0.94% and 1.79% for…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpeech Recognition and Synthesis · Speech and Audio Processing
MethodsFocus
