  The performance of speaker verification (SV) models may drop dramatically in
noisy environments. A speech enhancement (SE) module can be used as a front-end
strategy. However, existing SE methods may fail to bring performance
improvements to downstream SV systems due to artifacts in the predicted signals
of SE models. To compensate for artifacts, we propose a generic denoising
framework named LC4SV, which can serve as a pre-processor for various unknown
downstream SV models. In LC4SV, we employ a learning-based interpolation agent
to automatically generate the appropriate coefficients between the enhanced
signal and its noisy input to improve SV performance in noisy environments. Our
experimental results demonstrate that LC4SV consistently improves the
performance of various unseen SV systems. To the best of our knowledge, this
work is the first attempt to develop a learning-based interpolation scheme
aiming at improving SV performance in noisy environments.
