## 1 Introduction  
In recent years, advancements in computer technology and artificial intelligence (AI) have significantly enhanced human life, with automatic speech recognition (ASR) serving as a prominent example. Language, being the most natural and effective form of human communication, has positioned ASR as a key component in intelligent human-computer interaction systems. Human communication, inherently multimodal, involves not only speech but also visual cues such as mouth movements and body language, which play an essential role in conveying information [1, 2, 3]. Audio-visual speech recognition (AVSR) represents a form of multimodal speech recognition that enhances the accuracy of speech recognition systems by integrating both audio signals and the speaker's facial cues during speech production [4, 5, 6]. In the early stages of AVSR research, the hidden Markov model (HMM) was commonly employed to model the temporal correlation between audio and visual data [7, 8, 9]. However, with the advent of deep learning, substantial progress has been made in the field. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks, have proven effective in modeling sequential video data and extracting temporal features [10, 11, 12]. [PERSON] et al. [13] proposed a multimodal RNN model, using LSTM RNNs for processing audio features while combining convolutional neural networks (CNNs)and LSTMs for visual modalities. This model was validated on the AVletters dataset, demonstrating its effectiveness in multimodal feature extraction. [PERSON] et al. [10] utilized a five-layer bidirectional LSTM (BiLSTM) encoder to process both audio and visual features, coupled with a two-layer unidirectional LSTM decoder for generating predictions for character sets. The LSTM's gating mechanism efficiently captures temporal dependencies, mitigating issues such as gradient vanishing and improving model robustness in noisy environments and overlapping speech scenarios. [PERSON] et al. [14] developed an end-to-end visual speech recognition system based on LSTM networks, which extracted features from lip regions and difference images. They incorporated an additional LSTM layer to model temporal dynamics, using bidirectional LSTMs for stream fusion. In 2017, Google and the University of Toronto proposed the Transformer model [15], which utilizes an attention mechanism in both its encoder and decoder. Notably, the Transformer replaced the traditional RNN with attention mechanisms in the encoder, resulting in superior performance in machine translation tasks. Researchers later extended the Transformer to end-to-end speech recognition systems, achieving remarkable improvements [16; 17; 18]. [PERSON] et al. [19] compared two Transformer-based self-attentive lip-reading models, one utilizing CTC loss and the other using sequence-to-sequence loss, and found that the latter approach yielded superior performance. [PERSON] et al. [17] incorporated an attentional fusion block in the encoder to enrich feature representations by leveraging the correlations between audio-visual modalities, which reduced the over-reliance on audio signals. [PERSON] et al. [20] proposed a novel AVSR model with a unified cross-modal attention mechanism, which does not require forced alignment. This flexible approach uses six Transformer blocks in the decoder, demonstrating robustness even in the absence of a visual modality or when dealing with unaligned audio-visual frames in experiments on the LRS3 dataset. However, the global attention mechanism of the Transformer requires computing attention weights across all positions when processing long sequences. This can lead to attention weight dispersion, making it difficult to effectively capture important dependencies. Furthermore, excessive computation in non-critical regions may obscure essential information, thus reducing model performance. To address these challenges, we propose a Transformer model that incorporates a sparse attention mechanism.