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20182024
most citedASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan

131 citations · 143 across the 14 of their papers we have counts for

collaborators

15 papers

eess.AS2022

I4U System Description for NIST SRE'20 CTS Challenge

Kong Aik Lee, Tomi Kinnunen, Daniele Colibro +23

This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resu…

eess.AS2022

Baselines and Protocols for Household Speaker Recognition

Alexey Sholokhov, Xuechen Liu, Md Sahidullah +1

Speaker recognition on household devices, such as smart speakers, features several challenges: (i) robustness across a vast number of heterogeneous domains (households), (ii) short…

cs.SD2022

Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation

Xuechen Liu, Md Sahidullah, Tomi Kinnunen

In this paper, we initiate the concern of enhancing the spoofing robustness of the automatic speaker verification (ASV) system, without the primary presence of a separate counterme…

cs.SD20221 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…

cs.SD2022

Learnable Nonlinear Compression for Robust Speaker Verification

Xuechen Liu, Md Sahidullah, Tomi Kinnunen

In this study, we focus on nonlinear compression methods in spectral features for speaker verification based on deep neural network. We consider different kinds of channel-dependen…

cs.SD20214 cited

Optimizing Multi-Taper Features for Deep Speaker Verification

Xuechen Liu, Md Sahidullah, Tomi Kinnunen

Multi-taper estimators provide low-variance power spectrum estimates that can be used in place of the windowed discrete Fourier transform (DFT) to extract speech features such as m…