## 2 Related Work
### Research Difficulties
However, while the model excels in noisy conditions, its reliance on multimodal data increases the complexity and data requirements for implementation. Further validation is needed to assess its performance in scenarios lacking visual information. [PERSON] et al. [28] proposed a Conformer variant that combines convolutional neural networks and Transformers, demonstrating performance improvements on the AISHELL-1 and LibriSpeech datasets. The results indicated significant gains in recognition accuracy and training speed. However, the complex structure of this model increases training difficulty and hardware resource requirements. Moreover, while the multi-level design enhances the model's expressive ability, it also introduces more hyperparameters, complicating the tuning process. [PERSON] et al. [29] developed Conformer-LHUC, a Conformer model combined with Learning Hidden Unit Contributions (LHUCs) for speaker adaptation. Experiments demonstrated that Conformer-LHUC performed well in elderly speech recognition and showed potential in the clinical diagnosis and treatment of Alzheimer's disease. Despite its strong performance in specific applications, the adaptability and generality of Conformer-LHUC in other scenarios have not yet been fully validated. Moreover, the inclusion of the LHUC mechanism increases model complexity and computational cost, which may limit its use in resource-constrained environments. [PERSON] et al. [30] proposed a Mandarin audio-visual speech recognition model based on adaptive decision fusion and tone features, which combines audio and visual information to improve recognition performance. Experimental results showed that the model significantly enhanced the system's robustness, particularly in noisy environments, compared to single-modal models. Similarly, another study [31] focused on emotion recognition using speech and visual modalities, introducing a multi-learning framework that effectively fuses information from both modalities to achieve precise emotion modeling. Although the multimodal model excels in noisy settings, its reliance on multimodal data limits its applicability in scenarios where visual information is unavailable. While end-to-end models, characterized by their simplified architecture and the application of advanced deep learning algorithms, have substantially enhanced the accuracy of speech recognition, several challenges persist. Specifically, due to their reliance on deep learning neural networks, these models are prone to issues such as overfitting and the tendency to converge to local optima during training. Additionally, the large-scale nature of end-to-end models often involves a considerable number of parameters and intricate nonlinear relationships, which complicate the optimization process. Consequently, achieving a global optimum in such models remains a significant challenge. Furthermore, this challenge is not confined to speech recognition alone; researchers employing deep learning models across various domains face similar issues. [PERSON] et al. [32]highlighted that while the Transformer model can achieve excellent performance in speech recognition tasks due to its complex and deep structure, the computational cost associated with its numerous parameters significantly increases, leading to difficulties in model training and a tendency to fall into local optima. [PERSON] and [PERSON] [33] discovered that even advanced sharpness-aware minimization techniques fail to fully converge to the optimal solution, implying that even with sophisticated optimization strategies, avoiding local optima remains challenging. [PERSON] [34] proposed a method that frames the optimization of unknown linear functions as a zero-sum game between two players, utilizing no-regret learners to compute strategies for each round in an attempt to address this problem. However, this approach still faces high computational complexity in practical applications and is limited in effectiveness when handling large-scale data, failing to completely resolve the issue of local optima.