# Self-supervised speaker embeddings

## TL;DR

This paper introduces a self-supervised training method for speaker embeddings that leverages unlabelled data by reconstructing speech frames, improving generalization and performance on speaker recognition tasks.

## Contribution

It proposes a novel self-supervised training strategy with a decoder network that enhances speaker embeddings using unlabelled data, outperforming traditional methods.

## Key findings

- Significant performance improvements on VoxCeleb and Speakers in the Wild datasets.
- Effective use of unlabelled data through reconstruction loss.
- Regularization effect improves generalization to unseen speakers.

## Abstract

Contrary to i-vectors, speaker embeddings such as x-vectors are incapable of leveraging unlabelled utterances, due to the classification loss over training speakers. In this paper, we explore an alternative training strategy to enable the use of unlabelled utterances in training. We propose to train speaker embedding extractors via reconstructing the frames of a target speech segment, given the inferred embedding of another speech segment of the same utterance. We do this by attaching to the standard speaker embedding extractor a decoder network, which we feed not merely with the speaker embedding, but also with the estimated phone sequence of the target frame sequence. The reconstruction loss can be used either as a single objective, or be combined with the standard speaker classification loss. In the latter case, it acts as a regularizer, encouraging generalizability to speakers unseen during training. In all cases, the proposed architectures are trained from scratch and in an end-to-end fashion. We demonstrate the benefits from the proposed approach on VoxCeleb and Speakers in the wild, and we report notable improvements over the baseline.