Pairwise Discriminative Neural PLDA for Speaker Verification

20 Jan 2020Shreyas RamojiPrashant Krishnan VPrachi SinghSriram Ganapathy

The state-of-art approach to speaker verification involves the extraction of discriminative embeddings like x-vectors followed by a generative model back-end using a probabilistic linear discriminant analysis (PLDA). In this paper, we propose a Pairwise neural discriminative model for the task of speaker verification which operates on a pair of speaker embeddings such as x-vectors/i-vectors and outputs a score that can be considered as a scaled log-likelihood ratio... (read more)

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