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I-Vector Transformation Using K-Nearest Neighbors For Speaker Verification

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IEEE ICASSP 2020 Virtual Conference May 2020

Probabilistic Linear Discriminant Analysis (PLDA) is the most efficient backend for i-vectors. However, it requires labeled background data which can be difficult to access in practice. Unlike PLDA, cosine scoring avoids speaker-labels at the cost of degr

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I-Vector Transformation Using K-Nearest Neighbors For Speaker Verification

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Probabilistic Linear Discriminant Analysis (PLDA) is the most efficient backend for i-vectors. However, it requires labeled background data which can be difficult to access in practice. Unlike PLDA, cosine scoring avoids speaker-labels at the cost of degr