Robust Channel Learning for Large-Scale Radio Speaker Verification
This work addresses speaker verification for radio communications, offering incremental improvements in robustness and efficiency for this specific domain.
The authors tackled the problem of speaker verification in radio communications, which is challenging due to bandwidth constraints and noise, by proposing a Channel Robust Speaker Learning framework that enhances robustness through data augmentation and efficient fine-tuning, resulting in improved performance and mitigation of degradation in radio scenarios.
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.