  Speech enhancement aims to improve the perceptual quality of the speech
signal by suppression of the background noise. However, excessive suppression
may lead to speech distortion and speaker information loss, which degrades the
performance of speaker embedding extraction. To alleviate this problem, we
propose an end-to-end deep learning framework, dubbed PL-EESR, for robust
speaker representation extraction. This framework is optimized based on the
feedback of the speaker identification task and the high-level perceptual
deviation between the raw speech signal and its noisy version. We conducted
speaker verification tasks in both noisy and clean environment respectively to
evaluate our system. Compared to the baseline, our method shows better
performance in both clean and noisy environments, which means our method can
not only enhance the speaker relative information but also avoid adding
distortions.
