# Contrastive Predictive Coding Based Feature for Automatic Speaker Verification

arXiv: https://arxiv.org/abs/1904.01575 · HTML: https://mlsanity.com/paper/1904.01575/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 15 of 15)

- [ReSSL: Relational Self-Supervised Learning with Weak Augmentation](https://mlsanity.com/paper/2107.09282/) (2021)
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- [PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition](https://mlsanity.com/paper/2106.05933/) (2021)
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- [Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology](https://mlsanity.com/paper/2212.12105/) (2022)
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- [Pose Uncertainty Aware Movement Synchrony Estimation via Spatial-Temporal Graph Transformer](https://mlsanity.com/paper/2208.01161/) (2022)
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## Abstract (arXiv)

This thesis describes our ongoing work on Contrastive Predictive Coding (CPC) features for speaker verification. CPC is a recently proposed representation learning framework based on predictive coding and noise contrastive estimation. We focus on incorporating CPC features into the standard automatic speaker verification systems, and we present our methods, experiments, and analysis. This thesis also details necessary background knowledge in past and recent work on automatic speaker verification systems, conventional speech features, and the motivation and techniques behind CPC.
