# NPLDA: A Deep Neural PLDA Model for Speaker Verification

arXiv: https://arxiv.org/abs/2002.03562 · HTML: https://mlsanity.com/paper/2002.03562/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification](https://mlsanity.com/paper/2109.12058/) (2021)
- [Optimizing Tandem Speaker Verification and Anti-Spoofing Systems](https://mlsanity.com/paper/2201.09709/) (2022)
- [Learnable Nonlinear Compression for Robust Speaker Verification](https://mlsanity.com/paper/2202.05236/) (2022)
- [I4U System Description for NIST SRE'20 CTS Challenge](https://mlsanity.com/paper/2211.01091/) (2022)

## Abstract (arXiv)

The state-of-art approach for speaker verification consists of a neural network based embedding extractor along with a backend generative model such as the Probabilistic Linear Discriminant Analysis (PLDA). In this work, we propose a neural network approach for backend modeling in speaker recognition. The likelihood ratio score of the generative PLDA model is posed as a discriminative similarity function and the learnable parameters of the score function are optimized using a verification cost. The proposed model, termed as neural PLDA (NPLDA), is initialized using the generative PLDA model parameters. The loss function for the NPLDA model is an approximation of the minimum detection cost function (DCF). The speaker recognition experiments using the NPLDA model are performed on the speaker verificiation task in the VOiCES datasets as well as the SITW challenge dataset. In these experiments, the NPLDA model optimized using the proposed loss function improves significantly over the state-of-art PLDA based speaker verification system.