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A Principle Solution for Enroll-Test Mismatch in Speaker Recognition
Mismatch between enrollment and test conditions causes serious performan...
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Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification
Forensic audio analysis for speaker verification offers unique challenge...
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A discriminative condition-aware backend for speaker verification
We present a scoring approach for speaker verification that mimics the s...
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Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions
The performance of speaker verification systems degrades when vocal effo...
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Cross-Domain Face Verification: Matching ID Document and Self-Portrait Photographs
Cross-domain biometrics has been emerging as a new necessity, which pose...
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On the limits of cross-domain generalization in automated X-ray prediction
This large scale study focuses on quantifying what X-rays diagnostic pre...
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Small footprint Text-Independent Speaker Verification for Embedded Systems
Deep neural network approaches to speaker verification have proven succe...
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Squeezing value of cross-domain labels: a decoupled scoring approach for speaker verification
Domain mismatch often occurs in real applications and causes serious performance reduction on speaker verification systems. The common wisdom is to collect cross-domain data and train a multi-domain PLDA model, with the hope to learn a domain-independent speaker subspace. In this paper, we firstly present an empirical study to show that simply adding cross-domain data does not help performance in conditions with enrollment-test mismatch. Careful analysis shows that this striking result is caused by the incoherent statistics between the enrollment and test conditions. Based on this analysis, we present a decoupled scoring approach that can maximally squeeze the value of cross-domain labels and obtain optimal verification scores when the enrollment and test are mismatched. When the statistics are coherent, the new formulation falls back to the conventional PLDA. Experimental results on cross-channel test show that the proposed approach is highly effective and is a principle solution to domain mismatch.
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