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20192023
most citedCN-CELEB: a challenging Chinese speaker recognition dataset

13 citations · 26 across the 11 of their papers we have counts for

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Showing cs.SDShow all

8 papers · 1 filter

cs.SD2023★ 7 cited

The defender's perspective on automatic speaker verification: An overview

Haibin Wu, Jiawen Kang, Lingwei Meng +2

Automatic speaker verification (ASV) plays a critical role in security-sensitive environments. Regrettably, the reliability of ASV has been undermined by the emergence of spoofing…

cs.SD2023★ 1 cited

Unified Modeling of Multi-Talker Overlapped Speech Recognition and Diarization with a Sidecar Separator

Lingwei Meng, Jiawen Kang, Mingyu Cui +3

Multi-talker overlapped speech poses a significant challenge for speech recognition and diarization. Recent research indicated that these two tasks are inter-dependent and compleme…

cs.SD2023★ 1 cited

A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker One

Lingwei Meng, Jiawen Kang, Mingyu Cui +3

Although automatic speech recognition (ASR) can perform well in common non-overlapping environments, sustaining performance in multi-talker overlapping speech recognition remains c…

cs.SD2022

Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion

Haibin Wu, Jiawen Kang, Lingwei Meng +5

Recent years have witnessed the extraordinary development of automatic speaker verification (ASV). However, previous works show that state-of-the-art ASV models are seriously vulne…

cs.SD2022★ 1 cited

Spoofing-Aware Speaker Verification by Multi-Level Fusion

Haibin Wu, Lingwei Meng, Jiawen Kang +5

Recently, many novel techniques have been introduced to deal with spoofing attacks, and achieve promising countermeasure (CM) performances. However, these works only take the stand…

cs.SD2020

A Principle Solution for Enroll-Test Mismatch in Speaker Recognition

Lantian Li, Dong Wang, Jiawen Kang +4

Mismatch between enrollment and test conditions causes serious performance degradation on speaker recognition systems. This paper presents a statistics decomposition (SD) approach…