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The performance of speaker verification (SV) models may drop dramatically innoisy environments. A speech enhancement (SE) module can be used as a front-endstrategy. However, existing SE methods may fail to bring performanceimprovements to downstream SV systems due to artifacts in the predicted signalsof SE models. To compensate for artifacts, we propose a generic denoisingframework named LC4SV, which can serve as a pre-processor for various unknowndownstream SV models. In LC4SV, we employ a learning-based interpolation agentto automatically generate the appropriate coefficients between the enhancedsignal and its noisy input to improve SV performance in noisy environments. Ourexperimental results demonstrate that LC4SV consistently improves theperformance of various unseen SV systems. To the best of our knowledge, thiswork is the first attempt to develop a learning-based interpolation schemeaiming at improving SV performance in noisy environments.

LC4SV: A Denoising Framework Learning to Compensate for Unseen Speaker Verification Models