## 2 Related Work
### Research Difficulties  
End-to-end models have significantly improved the accuracy of speech recognition due to their simplified architecture and powerful deep learning algorithms. In recent years, much research has focused on this promising approach. However, there remain certain limitations and areas for improvement, particularly in optimizing model architecture performance and decoding search algorithms. [PERSON] et al. (2018) in the OpenASR21 Challenge, collected external text models to train a text-to-speech model that generates data matching speech-to-text. They trained and fused various models (CNN, DNN, ResNet, etc. ), ultimately improving speech recognition performance under low-resource conditions. However, since they used a traditional hybrid DNN-HMM framework, the complex structure significantly reduced efficiency in both training and recognition compared to end-to-end models. [PERSON] et al. (2020) applied a DNN-HMM model architecture to recognize remote multi-microphone Mandarin audiovisual data. They conducted comprehensive ablation studies on all audio and video data in the purely audio, purely video, and audiovisual systems. The results revealed that the video modality supplemented the acoustics degraded by noise, reducing deletion errors and providing discriminative information in overlapping speech, thereby reducing substitution errors. While they explored end-to-end model structures toward the end of the paper, the results were not satisfactory, indicating the need for further research. [PERSON] et al. proposed a network structure that randomly perturbs acoustic features to obtain speaker representation vectors that are insensitive to speech order through joint learning. They first segmented audio features into various length scales, randomly perturbed the audio feature segments at each scale, and then concatenated them back into audio features consistent with the original audio duration. The original and perturbed audio features were then fed into two similar neural networks. Finally, they added a Kullback-Leibler (KL) divergence loss function to the original target loss function to drive model training ([PERSON] et al., 2018). This approach yielded good experimental results on the VoxCeleb corpus. However, using a residual neural network (ResNet), which is similar in structure to a convolutional neural network (CNN), presents limitations. Compared to end-to-end structures, ResNet is less effective in capturing long-range dependencies and global information than attention-based models. [PERSON] [24] proposed an improved Transformer-based end-to-end speech recognition model by integrating connectionist temporal classification (CTC) and a language model (LM) to further enhance recognition accuracy. Although this approach effectively improved recognition accuracy, a single model structure still faces the challenges of overfitting and local optima. Additionally, while integrating CTC and LM increases model robustness, it also adds to the computational complexity and training time, affecting the model's efficiency in real-time applications. [PERSON] [25] introduced a deep sparse Conformer model to improve long-sequence representation capabilities. Experiments demonstrated that the deep sparse Conformer performed well in handling long audio sequences. [PERSON] and [PERSON] [26] proposed an RNN-Transducer model using sparse self-attention layers to process out-of-domain audio, with experiments showing significantly improved recognition performance across different audio domains. Despite the deep sparse Conformer's superior performance in long-sequence processing, its implementation and training process are complex, requiring substantial computational resources. Additionally, its performance in short-sequence and real-time applications needs further exploration. [PERSON] and [PERSON] [27] developed a Conformer model for efficient audio and visual processing, enhancing noise robustness. Experiments showed that the model significantly improved speech recognition performance in noisy environments.