A Stage-Wise Learning Strategy with Fixed Anchors for Robust Speaker Verification

TL;DR

This paper introduces a stage-wise learning strategy with fixed anchors to improve the robustness of speaker verification systems under noisy conditions, by separating discrimination and noise invariance learning.

Contribution

It proposes a novel anchor-based stage-wise training approach that enhances noise robustness while maintaining speaker discrimination, outperforming traditional joint optimization methods.

Findings

Improved speaker verification accuracy in noisy environments.

Enhanced robustness to various noise conditions.

Better preservation of speaker identity under distortion.

Abstract

Learning robust speaker representations under noisy conditions presents significant challenges, which requires careful handling of both discriminative and noise-invariant properties. In this work, we proposed an anchor-based stage-wise learning strategy for robust speaker representation learning. Specifically, our approach begins by training a base model to establish discriminative speaker boundaries, and then extract anchor embeddings from this model as stable references. Finally, a copy of the base model is fine-tuned on noisy inputs, regularized by enforcing proximity to their corresponding fixed anchor embeddings to preserve speaker identity under distortion. Experimental results suggest that this strategy offers advantages over conventional joint optimization, particularly in maintaining discrimination while improving noise robustness. The proposed method demonstrates consistent improvements across various noise conditions, potentially due to its ability to handle boundary stabilization and variation suppression separately.