Improving Speaker Recognition in Environmental Noise with Adaptive Filter

Abstract

Speaker recognition is challenging in real-world environments. Typically, studies approach noises only in an additive manner. However, real environments commonly present everberating conditions that worsen speech processing. When not considering reverberation in the system modeling, the system may not be robust when applied to real-world conditions. In this work, we use a slight different approach to simulate reverberation, considering randomized conditions of the environment. With this approach, each VoxCeleb1 test sample is corrupted by randomly generated conditions, with diversified amplitudes of noise and speech. We generate a corrupted dataset, in which the best model EER degraded from 0.93% to 30.13%. To improve this degradation, we propose using Normalized Kernel Least-Mean-Square (NKLMS) adaptive filter. Through the use of NKLMS, we were able to improve the EER from 30.13% to 1.11%. The results indicate that NKLMS has a great potential for speech enhancement to improve speaker recognition.