## 1 Introduction  
Speaker recognition encompasses tasks such as verification, identification, and diarization [1, 2, 3]. The process involves the analysis of voiceprint features extracted from speech, which are shaped by individual variations in vocal tract structure, pronunciation, and accent [4]. Comparable to fingerprint, facial, and iris recognition, voiceprint recognition offers the benefits of contactless operation and high accuracy. It finds extensive application in access control, user authentication, and forensic evidence collection, and has been integrated into various smart microphone systems, including cellphones, smart homes, and smart vehicles [3; 5; 6; 7; 8]. Recently, deep neural networks have achieved significant advancements in speaker recognition [9]. Many studies have focused on enhancing feature extraction by using advanced DNN architectures, such as Time-Delay Neural Networks (TDNNs) [10] and Residual Networks (ResNets) [11]. The X-Vector model, proposed by [10], was introduced as an improvement over the D-Vector model [12]. Unlike the frame-by-frame modeling approach employed in the D-Vector model, the X-Vector model utilizes a pooling layer to capture speech embedding units. It also incorporates data augmentation techniques to enhance the performance of TDNN-based speaker recognition systems. In further research on speaker recognition tasks, feature extraction models based on Deep Neural Networks (DNNs) incorporate various architectures [5; 13; 14; 15; 16; 17]. For instance, ref. [18] utilizes Convolutional Neural Networks (CNNs) to locally extract features from speech, while [19] employs Recurrent Neural Networks (RNNs) to capture temporal dependencies and patterns within the speech. Specifically, [PERSON]. [20] innovated the X-Vector approach by incorporating techniques from computer vision, namely the Squeeze Excitation network, Res2 Net, and the channel propagation aggregation mechanism. This integration significantly improved speaker recognition performance, marking it as one of the most effective systems of its time. In environments with an ideal sound field, which are free from ambient noise, sensing noise, interference from other speakers, and echoes, numerous deep neural network approaches for speaker recognition have demonstrated high performance in critical metrics such as EER and MinDCF. However, in actual sound field conditions, it is typical to experience sudden occurrences of high-energy noise and significant speech reverberation. For instance, in a confined conference room, sounds such as doors opening and closing, coughing, and footsteps are commonly present. Furthermore, in scenarios where the sound source is distant, the SNR between speech and the noise generated by acoustic sensor equipment is also low. This results in a marked reduction in the effectiveness of speaker recognition techniques, as reported in [21]. Preprocessing of the original speech, which includes the extraction of mel-spectrogram, spectrogram, and MFCC (mel frequency cepstrum coefficient), is essential prior to input into the back-end model. The occurrence of noise and reverberation leads to alterations in the speech's spectral characteristics. To solve this problem, scholars focus on speech preprocessing, feature extraction, loss function design, and the integration of a speech separation frontend [22; 23; 24; 25; 26]. For example, ref. [26] introduced the integration of FiLM into a Voice-Filter frontend to enhance the robustness of automatic speech recognition (ASR) and text-independent speaker verification (TI-SV) in noisy and reverberant conditions. These strategies primarily address specific noisy scenarios. In recent years, self-supervised learning (SSL) has achieved significant success in fields such as computer vision and natural language processing [27; 28; 29; 30; 31]. SSL can extract generalized information from unlabeled data without relying on labels, annotations, or structured text [32]. Refs.