# Self-Supervised Open-Set Speaker Recognition with Laguerre–Voronoi Descriptors

## TL;DR

This paper introduces a new self-supervised method for open-set speaker recognition using geometric properties of speaker data.

## Contribution

The novel contribution is a self-supervised framework using Laguerre–Voronoi descriptors for robust open-set speaker recognition.

## Key findings

- The proposed system outperforms state-of-the-art methods in open-set speaker recognition.
- The method achieves better cluster representation using a specialized clustering criterion.
- The framework leverages geometric properties of speaker distribution for improved verification.

## Abstract

Speaker recognition is a challenging problem in behavioral biometrics that has been rigorously investigated over the last decade. Although numerous supervised closed-set systems inherit the power of deep neural networks, limited studies have been made on open-set speaker recognition. This paper proposes a self-supervised open-set speaker recognition that leverages the geometric properties of speaker distribution for accurate and robust speaker verification. The proposed framework consists of a deep neural network incorporating a wider viewpoint of temporal speech features and Laguerre–Voronoi diagram-based speech feature extraction. The deep neural network is trained with a specialized clustering criterion that only requires positive pairs during training. The experiments validated that the proposed system outperformed current state-of-the-art methods in open-set speaker recognition and cluster representation.