###### Abstract
[15] improves the performance of traditional DNN x-vector language identification by using convolutional and Convolutional Long Short-Term Memory (CLSTM) neural networks to capture longer-term dependencies and enhance model feature extraction capabilities. ResNet [21] introduces residual connections to preserve original features, making the learning process smoother and more stable. ECAPA-TDNN [22] further enhances the performance of TDNN on speaker tasks by introducing additional skip connections. Both models have been introduced into language recognition due to their outstanding performance [23; 24; 25; 26]. While CNN-based models are effective in speech processing tasks, their limited kernel size often restricts them to capturing only local context, this is particularly disadvantageous for LID tasks, where the quantity of available context is crucial [27]. Recently, the self-attention mechanism of Transformer models [13; 28] which can simultaneously consider information from all positions and thereby more comprehensively model contextual relationships and improve the model's understanding of sequences, has been introduced into language identification. However, while modeling global information, it tends to overlook finer local details. To address this issue, the conformer model [29], which efficiently parameterizes local and global correlations in audio sequences, has been proposed. Nevertheless, this static single-branch architecture is difficult to interpret and modify, and it incurs greater inference difficulty and computational costs. In the field of Computer Vision (CV), a parallel hybrid network structure has been proposed [30], combining convolution operations and self-attention mechanisms to enhance representation learning, with the parallel structure maximizing the preservation of both local and global features. In the domain of ASR, a parallel dual-branch structure has been proposed [31], which compared to the conformer model, has lower computational complexity, greater flexibility, and performs optimally. Inspired by these works, we introduce a novel model architecture called branch-transformer for language identification tasks. It consists of parallel dual-branch structures. One branch employs self-attention [32] to capture global information, while the other branch utilizes a Convolutional Gated Multi-Layer Perceptron module [33] to extract local information. Furthermore, to fully utilize both global and local information, we explore various fusion methods. Compared to previous approaches, the main contributions of this paper are as follows:  
1. We integrated a dual-branch structure into the language identification model, incorporating both a self-attention-based transformer module and a convolutional branch module. This enables the optimized model to simultaneously capture global and local contextual dependencies, ultimately enhancing the accuracy of language identification. 2. To optimize the utilization of both global and local information, we present six distinct information fusion techniques. Through a comparative analysis, we assess the influence of these methods on language identification. 3. Through extensive experiments, we have demonstrated the effectiveness of the dual-branch architecture and information fusion. We have achieved the best performance on different length test sets of LRE17 (MLS14, 3 s, 10 s, 30 s). The subsequent content of this paper is organized as follows: Section 2 describes our methodology; Section 3 introduces the experimental data and parameter settings of the models; Section 4 presents the experimental results and analysis. The conclusion is drawn in Section 5.