Graph Neural Network Backend for Speaker Recognition

TL;DR

This paper introduces a graph neural network backend for speaker recognition that leverages local relationships among embeddings, significantly improving recognition accuracy over traditional similarity-based methods.

Contribution

It proposes a novel GNN-based backend that models embeddings as nodes in a graph, capturing latent relationships to enhance speaker recognition performance.

Findings

GNN backend outperforms traditional methods on multiple datasets.

Different graph settings and GNN variants improve recognition accuracy.

Experimental results demonstrate significant performance gains.

Abstract

Currently, most speaker recognition backends, such as cosine, linear discriminant analysis (LDA), or probabilistic linear discriminant analysis (PLDA), make decisions by calculating similarity or distance between enrollment and test embeddings which are already extracted from neural networks. However, for each embedding, the local structure of itself and its neighbor embeddings in the low-dimensional space is different, which may be helpful for the recognition but is often ignored. In order to take advantage of it, we propose a graph neural network (GNN) backend to mine latent relationships among embeddings for classification. We assume all the embeddings as nodes on a graph, and their edges are computed based on some similarity function, such as cosine, LDA+cosine, or LDA+PLDA. We study different graph settings and explore variants of GNN to find a better message passing and aggregation way to accomplish the recognition task. Experimental results on NIST SRE14 i-vector challenging, VoxCeleb1-O, VoxCeleb1-E, and VoxCeleb1-H datasets demonstrate that our proposed GNN backends significantly outperform current mainstream methods.