  Background noise is a well-known factor that deteriorates the accuracy and
reliability of speaker verification (SV) systems by blurring speech
intelligibility. Various studies have used separate pretrained enhancement
models as the front-end module of the SV system in noisy environments, and
these methods effectively remove noises. However, the denoising process of
independent enhancement models not tailored to the SV task can also distort the
speaker information included in utterances. We argue that the enhancement
network and speaker embedding extractor should be fully jointly trained for SV
tasks under noisy conditions to alleviate this issue. Therefore, we proposed a
U-Net-based integrated framework that simultaneously optimizes speaker
identification and feature enhancement losses. Moreover, we analyzed the
structural limitations of using U-Net directly for noise SV tasks and further
proposed Extended U-Net to reduce these drawbacks. We evaluated the models on
the noise-synthesized VoxCeleb1 test set and VOiCES development set recorded in
various noisy scenarios. The experimental results demonstrate that the
U-Net-based fully joint training framework is more effective than the baseline,
and the extended U-Net exhibited state-of-the-art performance versus the
recently proposed compensation systems.
