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Post-Training Embedding Alignment for Decoupling Enrollment and Runtime Speaker Recognition Models
Automated speaker identification (SID) is a crucial step for the personalization of a wide range of speech-enabled services.
Adversarial Reweighting for Speaker Verification Fairness
Results show that the pairwise weighting method can achieve 1. 08% overall EER, 1. 25% for male and 0. 67% for female speakers, with relative EER reductions of 7. 7%, 10. 1% and 3. 0%, respectively.
Fusion of Embeddings Networks for Robust Combination of Text Dependent and Independent Speaker Recognition
Based on whether the speech content is constrained or not, both text-dependent (TD) and text-independent (TI) speaker recognition models may be used.
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