## 2 Related Works  
In recent years, with the rapid development of neural networks, neural models have been widely used in speaker identification tasks. In this context, various speaker identification methods based on Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their variants have been proposed [12; 13; 14; 15; 16; 17]. [PERSON] et al. [18] proposed a frame-level acoustic feature model based on DNNs, where the average value of specific features from the last layer of a trained DNN is used as the speaker model, known as the d-vector. Compared to the traditional i-vector [19] method, the d-vector performs better in small-scale, text-dependent speaker identification tasks. To overcome the limitation of d-vector, which only extracts frame-level features, [PERSON] et al. [4] proposed the x-vector model. This model uses a multi-layer Time Delay Neural Network (TDNN) and a statistical pooling layer to transform frame-level features into sentence-level feature representations, which are then converted into speaker embeddings via a fully connected layer. In 2017, [PERSON] et al. [20] proposed a model based on Residual Networks (ResNets), which introduces residual connections into deep network models to extract more discriminative voiceprint features. However, the feature extraction method of ResNets is relatively simple. Building on residual networks, [PERSON] et al. [21] proposed the ResNet524L and ResNet5234V2 models, which employ convolution kernels of different scales to enhance the representation of multi-scale features. In 2020, [PERSON] et al. [5] further enhanced the TDNN-based x-vector architecture by proposing the ECAPA-TDNN model. In 2023, [PERSON] and [PERSON] [22] proposed ECAPA++, an enhanced ECAPA-TDNN model incorporating increased depth, recursive convolution, and a pyramid-based multi-path feature enhancement module. ECAPA++ achieved up to 25% relative improvement on VoxCeleb benchmarks while significantly reducing computational complexity, demonstrating efficiency comparable to state-of-the-art ResNet-based systems. In 2024, [PERSON] et al. [23] proposed SC-Ecapa Tdnn, an enhanced ECAPA-TDNN model integrating depth-separable convolutions and adaptive 1D convolution-based channel attention. This architecture improves speaker feature extraction while balancing computational efficiency. Experiments on AISHELL and CN-Celeb datasets demonstrate its superior performance over mainstream speaker recognition systems. Although significant progress has been made in speaker identification technology based on deep learning in recent years, with good performance in general scenarios, these models still face multiple challenges, such as complex noise interference and limited short speech information. In response to complex noise environments, [PERSON]. [24] proposed a bionic cepstral coefficient (BCC) method in 2024, which combines a bionic wavelet filter bank (BWFB) with the Equivalent Rectangular Bandwidth (ERB) rate scale for feature extraction, significantly improving the performance of speaker identification models in noisy environments. In 2024, [PERSON] [25] and others adopted feature optimization strategies, including genetic algorithms and marine predator algorithms, combined with feature fusion and dimensionality reduction techniques. These methods significantly improved speaker identification accuracy and robustness on various noisy datasets such as TIMIT and VoxCeleb1. However, these approaches are computationally expensive and require high computational resources and parameter tuning. In contrast, data augmentation methods, due to their simplicity and ability to mitigate the impact of complex noise on the model, are widely used. In 2023, [PERSON] et al. [26] introduced a theoretical learning curve for the multi-class Bayes classifier, demonstrating that data augmentation increases the sample size, which in turn reduces classification error by providing more diverse training samples.