## 1 Introduction
[32; 33; 34; 35; 36; 37] successively demonstrated the impressive translational results continuously achieved in ASR tasks using SSL. SSL is not only employed for ASR and phoneme recognition but also extends beyond acoustic character recognition to include tasks such as speaker recognition [36] and emotion recognition [37]. To overcome the challenges posed by noisy speech during preprocessing, ref. [38] introduced WavLM, which integrates a SSL model with a robust speaker recognition model to create a novel cascaded architecture. However, the model exhibits certain drawbacks: (1) Despite incorporating strategies such as mask learning and data augmentations involving speed, volume, and noise, the model's performance under conditions of strong interference consistently falls short compared to robust baseline models like ECAPA-TDNN [20]and CAM++ [39]; (2) In [32], the base HuBERT required 32 V100 GPUs to train on a dataset of 960 h of audio from the LibriSpeech. This level of resource usage is highly detrimental to the goals of miniaturization and low power consumption in smart devices. This study introduces a novel TDNN-based architecture incorporating a pre-trained model for robust speaker recognition in challenging noisy and reverberant conditions (PNR-TDNN). The architecture leverages the strengths of both SSL and TDNN. Firstly, the SSL model, inspired by HuBERT, is utilized in this study. Given that the number of pseudo-labels is typically a fixed hyperparameter for SSL models, the Canopy and Mini Batch K-Means++ algorithm is proposed to more accurately ascertain the appropriate number of pseudo-labels, thereby serving as dynamic adaptive filtering and extraction units for subsequent speaker recognition techniques. Secondly, inspired by the methods for extracting neighboring information in visual detection and semantic segmentation tasks [40; 41; 42; 43; 44; 45; 46; 47], this work recognizes the importance of processing inter-contextual channel information in speech features. To address this, the ECAPA-TDNN framework is modified by incorporating several enhancements, particularly in the TDNN and pooling layers, to more effectively address the challenges of noise and reverberation. The article's contributions are as follows:  
1. The development of a novel training strategy for the SSL model in the presence of strong interference, as well as the introduction of a miniaturized method for the integration of SSL model and speaker recognition model. 2. The pseudo-label generation mechanism of a typical SSL model is improved through the application of the Canopy and Mini Batch K-Means++ algorithms. 3. Enhancements are made to the pooling layer and Res2 SE module of the ECAPA-TDNN by incorporating multi-head attention-based statistic pooling, cross-channel fusion for residual learning, and a non-average full-channel processing mechanism. The rest of this paper is structured as follows: Section 2 introduces the typical SSL and speaker recognition model, as well as the proposed PNR-TDNN. Section 3 describes the details from dataset preparation to model training. Section 4 analyzes and compares the results of multiple sets of experiments. The conclusions are summarized in Section 5.