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Pairwise Discriminative Neural PLDA for Speaker Verification
- Publication Year :
- 2020
-
Abstract
- 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. We construct a differentiable cost function which approximates speaker verification loss, namely the minimum detection cost. The pre-processing steps of linear discriminant analysis (LDA), unit length normalization and within class covariance normalization are all modeled as layers of a neural model and the speaker verification cost functions can be back-propagated through these layers during training. We also explore regularization techniques to prevent overfitting, which is a major concern in using discriminative back-end models for verification tasks. The experiments are performed on the NIST SRE 2018 development and evaluation datasets. We observe average relative improvements of 8% in CMN2 condition and 30% in VAST condition over the PLDA baseline system.<br />Comment: This paper was submitted to IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2020. Link to GitHub Repository: https://github.com/iiscleap/NeuralPlda
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2001.07034
- Document Type :
- Working Paper