unreliability as the volume of communication increases. In this context, speaker recognition technology can effectively assist in the automation of air-ground communication analysis. By accurately distinguishing between the identities of controllers and pilots involved in the conversation, it can not only improve the efficiency of communication content parsing but also provide technical support for identifying potential communication errors, thus reducing the incidence of safety accidents and ensuring aviation safety. Speaker recognition (SR) technology is generally divided into two categories based on the application purpose: speaker identification [1] and speaker verification [1]. The task of speaker identification is to recognize the speaker of a particular segment of speech from multiple speakers, mainly used in speaker recognition scenarios involving multiple participants. On the other hand, speaker verification is the task of determining whether a segment of speech comes from a specific target speaker, commonly found in applications such as voiceprint locks and voiceprint authentication [2] for security purposes. Additionally, based on the working method of speaker recognition, it can be further classified into text-dependent [3] and text-independent [3] types. Text-dependent speaker recognition requires the speaker to record speech based on a specific text. Although this method has high recognition accuracy, its flexibility is limited, making it suitable only for scenarios with known text. In contrast, text-independent speaker recognition is unrestricted and can recognize speech from any arbitrary segment, making it highly flexible and convenient for widespread use in industrial applications. Given the diversity and uncertainty of speech in air-ground communication, as well as the complexity of situations involving multiple speakers in the channel, this study focuses on text-independent speaker identification technology to meet practical application needs. Currently, researchers have extensively studied deep learning-based speaker identification technology to replace traditional statistical models, aiming to improve recognition accuracy and robustness. [PERSON] et al. [4] proposed the x-vector model, which uses multilayer Time Delay Neural Networks (TDNNs) and a statistical pooling layer to transform frame-level features into sentence-level feature representations. These representations are then passed through a fully connected layer to obtain the speaker embedding, which effectively captures the speaker's identity features and is used to distinguish different speakers in subsequent recognition tasks. In 2020, [PERSON] et al. [5] enhanced the TDNN-based x-vector architecture by proposing the Emphasized Channel Attention, Propagation and Aggregation in TDNN (ECAPA-TDNN) model. This model incorporates improvements such as the Squeeze-and-Excitation Networks (SE-Net) module, channel attention mechanisms, and multi-layer feature fusion, significantly boosting the accuracy of speaker recognition. The ECAPA-TDNN model has since become one of the most advanced frameworks in the field of speaker recognition. However, these models perform sub-optimally in air-ground communication scenarios, primarily due to the distinct characteristics of air-ground communication, which differ significantly from general speech, presenting the following challenges for speaker recognition in air-ground communication:  
1. Air-ground communication is affected by complex background noise inside the cabin [6] and radio transmission interference, with noise exhibiting non-stationary characteristics and a low signal-to-noise ratio. This complex noise imposes higher demands on the robustness of the ECAPA-TDNN model, making it difficult for the model to extract stable speaker features. 2. The speech segments in air-ground communication are mostly shorter than 8 s, which are considered typical short speech.