Channel Adaptation for Speaker Verification Using Optimal Transport with Pseudo Label

TL;DR

This paper introduces a novel unsupervised domain adaptation method, JPOT-PL, that uses optimal transport and pseudo labels to effectively address channel mismatch in speaker verification, significantly improving performance.

Contribution

The paper presents a new unsupervised domain adaptation approach combining optimal transport with pseudo labels for better channel mismatch handling in speaker verification.

Findings

Reduces EER by over 10% compared to state-of-the-art methods

Effectively aligns distributions using geometric-aware optimal transport

Demonstrates robustness in real-world speaker verification scenarios

Abstract

Domain gap often degrades the performance of speaker verification (SV) systems when the statistical distributions of training data and real-world test speech are mismatched. Channel variation, a primary factor causing this gap, is less addressed than other issues (e.g., noise). Although various domain adaptation algorithms could be applied to handle this domain gap problem, most algorithms could not take the complex distribution structure in domain alignment with discriminative learning. In this paper, we propose a novel unsupervised domain adaptation method, i.e., Joint Partial Optimal Transport with Pseudo Label (JPOT-PL), to alleviate the channel mismatch problem. Leveraging the geometric-aware distance metric of optimal transport in distribution alignment, we further design a pseudo label-based discriminative learning where the pseudo label can be regarded as a new type of soft speaker label derived from the optimal coupling. With the JPOT-PL, we carry out experiments on the SV channel adaptation task with VoxCeleb as the basis corpus. Experiments show our method reduces EER by over 10% compared with several state-of-the-art channel adaptation algorithms.