Hyperbolic Additive Margin Softmax with Hierarchical Information for Speaker Verification

TL;DR

This paper introduces hyperbolic space-based softmax methods for speaker verification, effectively modeling hierarchical features and improving verification accuracy over traditional Euclidean approaches.

Contribution

It proposes Hyperbolic Softmax and HAM-Softmax that incorporate hierarchical information into speaker embeddings, enhancing inter-class separability and verification performance.

Findings

Achieved 27.84% relative EER reduction with H-Softmax.

Achieved 14.23% relative EER reduction with HAM-Softmax.

Code available at https://github.com/PunkMale/HAM-Softmax.

Abstract

Speaker embedding learning based on Euclidean space has achieved significant progress, but it is still insufficient in modeling hierarchical information within speaker features. Hyperbolic space, with its negative curvature geometric properties, can efficiently represent hierarchical information within a finite volume, making it more suitable for the feature distribution of speaker embeddings. In this paper, we propose Hyperbolic Softmax (H-Softmax) and Hyperbolic Additive Margin Softmax (HAM-Softmax) based on hyperbolic space. H-Softmax incorporates hierarchical information into speaker embeddings by projecting embeddings and speaker centers into hyperbolic space and computing hyperbolic distances. HAM-Softmax further enhances inter-class separability by introducing margin constraint on this basis. Experimental results show that H-Softmax and HAM-Softmax achieve average relative EER reductions of 27.84% and 14.23% compared with standard Softmax and AM-Softmax, respectively, demonstrating that the proposed methods effectively improve speaker verification performance and at the same time preserve the capability of hierarchical structure modeling. The code will be released at https://github.com/PunkMale/HAM-Softmax.