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20182026
most citedSASV Challenge 2022: A Spoofing Aware Speaker Verification Challenge Evaluation Plan

16 citations · 36 across the 22 of their papers we have counts for

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6 papers · 1 filter

eess.AS2022★ 1 cited

Diffusion-based Generative Speech Source Separation

Robin Scheibler, Youna Ji, Soo-Whan Chung +3

We propose DiffSep, a new single channel source separation method based on score-matching of a stochastic differential equation (SDE). We craft a tailored continuous time diffusion…

eess.AS2022

Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting

Hyeon-Kyeong Shin, Hyewon Han, Doyeon Kim +2

In this paper, we propose a novel end-to-end user-defined keyword spotting method that utilizes linguistically corresponding patterns between speech and text sequences. Unlike prev…

cs.SD2022★ 1 cited

Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion

Hye-jin Shim, Hemlata Tak, Xuechen Liu +12

Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent…

eess.AS2022★ 5 cited

SASV 2022: The First Spoofing-Aware Speaker Verification Challenge

Jee-weon Jung, Hemlata Tak, Hye-jin Shim +6

The first spoofing-aware speaker verification (SASV) challenge aims to integrate research efforts in speaker verification and anti-spoofing. We extend the speaker verification scen…

cs.SD2022★ 16 cited

SASV Challenge 2022: A Spoofing Aware Speaker Verification Challenge Evaluation Plan

Jee-weon Jung, Hemlata Tak, Hye-jin Shim +7

ASV (automatic speaker verification) systems are intrinsically required to reject both non-target (e.g., voice uttered by different speaker) and spoofed (e.g., synthesised or conve…

cs.SD2022

Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement

Doyeon Kim, Hyewon Han, Hyeon-Kyeong Shin +2

Modern neural speech enhancement models usually include various forms of phase information in their training loss terms, either explicitly or implicitly. However, these loss terms…