## 1 Introduction  
Dialect identification (DID) refers to the task of identifying the dialect category, which generally belongs to the same language family and can be considered as a special case of language identification. With the development of i-vector, x-vector, and neural networks, language identification has achieved significant success [1; 2; 3]. In recent years, accent and dialect identification have received increasing attention from speech researchers [4; 5; 6; 7; 8; 9]. However, DID is often more challenging than the language identification task, as similar dialects often share similar feature spaces [10]. Dialect identification technology is commonly applied to the front-end of speech processing systems, such as automatic speech recognition (ASR) and multilingual translation systems. Therefore, the development of DID technology is essential in the era of speech interaction intelligence. Currently, the x-vector-based architecture remains the most widely used method for DID [11]. The X-vector is a method that utilizes deep neural networks to extract features from speech signals, enabling the mapping of high-dimensional speech features to a fixed-length vector to represent speech information. Unlike traditional speech feature extraction methods, the X-vector is trained through a deep learning model and can better express the nonlinear characteristics of speech signals. With the continuous development of deep learning, numerous excellent x-vector architectures have been proposed for speech, including the time-delay neural network (TDNN) [12], extended time-delay neural network (E-TDNN) [13], and factorized time-delay neural network (F-TDNN) [14]. TDNN is a suitable approach for speech and time-series signal processing as it considers multiple consecutive frames of information during network input. E-TDNN utilizes a deeper network structure than TDNN to establish gradual context connections. F-TDNN decomposes the parameter matrix into smaller matrices, effectively reducing the layer parameters, and leveraging the advantages of network depth when overall parameters are comparable. Additionally, some researchers have proposed the ECAPA-TDNN architecture [15], which emphasizes channel attention, propagation, and aggregation, and has made significant progress in speaker recognition. Arabic dialect identification (ADI) is one of the two tasks in the latest MGB-5 challenge [16]. The task involves identifying audio categories from 17 Arabic dialect audios collected from YouTube. Arabic dialects belong to various dialects within the same language family, making their distinction challenging and more difficult than other easy-to-understand dialects. To enhance the performance of recognizing similar-sounding Arabic dialects, we propose a new network module named the multi-scale channel adaptive module (MSCA-Res2 Block). The MSCA-Res2 Block is designed to address issues in the ECAPA-TDNN, such as an insufficient receptive field, inflexible context information extraction, and inadequate local channel attention. It consists of two components, namely, multi-scale dilated convolutions and multi-scale channel attention. The former allows the network to capture receptive fields of different sizes to adapt to the varying speaking rates in different dialects, while the latter can dynamically calibrate channel attention features extracted at different scales. The unique aspect of the MSCA-Res2 Block design is that it combines both global and local attention mechanisms, which selectively amplify informative features and suppress irrelevant features, thus achieving more effective feature representation. Such feature representation is particularly critical for dialect identification tasks, as the network needs to capture subtle pronunciation and intonation differences. The MSCA-Res2 Block module can adapt to these changes and extract the most prominent features to improve the accuracy of recognition tasks. We use this module to improve the ECAPA-TDNN, which we call the MSCA-TDNN network.