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THUEE system description for NIST 2019 SRE CTS Challenge
This paper describes the systems submitted by the department of electron...
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Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020
In this report, we discribe the submission of Tongji University undergra...
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Automatic Quality Assessment for Audio-Visual Verification Systems. The LOVe submission to NIST SRE Challenge 2019
Fusion of scores is a cornerstone of multimodal biometric systems compos...
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Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020
This paper describes the Microsoft speaker diarization system for monaur...
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Tongji University Team for the VoxCeleb Speaker Recognition Challenge 2020
In this report, we describe the submission of Tongji University team to ...
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UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation
This document briefly describes the systems submitted by the Center for ...
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Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition
Lip motion reflects behavior characteristics of speakers, and thus can b...
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HLT-NUS Submission for NIST 2019 Multimedia Speaker Recognition Evaluation
This work describes the speaker verification system developed by Human Language Technology Laboratory, National University of Singapore (HLT-NUS) for 2019 NIST Multimedia Speaker Recognition Evaluation (SRE). The multimedia research has gained attention to a wide range of applications and speaker recognition is no exception to it. In contrast to the previous NIST SREs, the latest edition focuses on a multimedia track to recognize speakers with both audio and visual information. We developed separate systems for audio and visual inputs followed by a score level fusion of the systems from the two modalities to collectively use their information. The audio systems are based on x-vector based speaker embedding, whereas the face recognition systems are based on ResNet and InsightFace based face embeddings. With post evaluation studies and refinements, we obtain an equal error rate (EER) of 0.88 actual detection cost function (actDCF) of 0.026 on the evaluation set of 2019 NIST multimedia SRE corpus.
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