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Audio-visual Speaker Recognition with a Cross-modal Discriminative Network

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arxiv 2008.03894 v1 pith:4BO2FGV2 submitted 2020-08-10 eess.AS

Ruijie Tao , Rohan Kumar Das , Haizhou Li This is my paper
classification eess.AS
keywords speakeraudio-visualdiscriminativenetworkrecognitionvfnetcross-modalevaluation
verification ladder T0 review T1 audit T2 compute T3 formal
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Audio-visual speaker recognition is one of the tasks in the recent 2019 NIST speaker recognition evaluation (SRE). Studies in neuroscience and computer science all point to the fact that vision and auditory neural signals interact in the cognitive process. This motivated us to study a cross-modal network, namely voice-face discriminative network (VFNet) that establishes the general relation between human voice and face. Experiments show that VFNet provides additional speaker discriminative information. With VFNet, we achieve 16.54% equal error rate relative reduction over the score level fusion audio-visual baseline on evaluation set of 2019 NIST SRE.

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Cited by 1 Pith paper

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  1. Missing-Token Prompted Reliability-Aware Fusion for Robust Polyglot Speaker Identification

    cs.SD 2026-06 unverdicted novelty 5.0 of 10

    MRAF framework uses missing-token prompting and reliability-aware cross-attention fusion to achieve 100% accuracy on some POLY-SIM 2026 tasks and competitive results on missing-face cases.

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