Robust Channel Learning for Large-Scale Radio Speaker Verification

TL;DR

This paper introduces a robust speaker verification framework tailored for radio communications, employing data augmentation, noise handling, and efficient fine-tuning to improve accuracy under challenging channel conditions.

Contribution

It presents a novel Channel Robust Speaker Learning framework with data augmentation, noise modeling, and efficient transfer learning, plus a large-scale radio speech benchmark.

Findings

Enhanced speaker verification accuracy in radio scenarios.

Effective mitigation of bandwidth and noise effects.

Reduced training time with efficient fine-tuning.

Abstract

Recent research in speaker verification has increasingly focused on achieving robust and reliable recognition under challenging channel conditions and noisy environments. Identifying speakers in radio communications is particularly difficult due to inherent limitations such as constrained bandwidth and pervasive noise interference. To address this issue, we present a Channel Robust Speaker Learning (CRSL) framework that enhances the robustness of the current speaker verification pipeline, considering data source, data augmentation, and the efficiency of model transfer processes. Our framework introduces an augmentation module that mitigates bandwidth variations in radio speech datasets by manipulating the bandwidth of training inputs. It also addresses unknown noise by introducing noise within the manifold space. Additionally, we propose an efficient fine-tuning method that reduces the need for extensive additional training time and large amounts of data. Moreover, we develop a toolkit for assembling a large-scale radio speech corpus and establish a benchmark specifically tailored for radio scenario speaker verification studies. Experimental results demonstrate that our proposed methodology effectively enhances performance and mitigates degradation caused by radio transmission in speaker verification tasks. The code will be available on Github.