## 2 Related Work
### Research Process  
In the early stages of speech recognition, traditional models typically involved a complex sequence of processes, including capturing speech signals, feature processing, training acoustic and language models, performing statistical calculations, and decoding the output [7]. This intricate structure, along with the cumbersome process and extensive data handling, significantly impacted the accuracy and real-time performance of speech recognition systems. With the continuous development of machine learning, integrating deep learning with traditional Hidden Markov Models (HMMs) has become a popular trend. [PERSON] et al. focused on the application of deep neural networks (DNNs) in noisy and robust environments, finding that DNNs outperformed traditional Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) systems in these settings [8]. [PERSON] et al. [9] improved the learning process of DNN-HMM by generating a large number of pseudo-samples to address the issue of insufficient data. Their experiments demonstrated that the DNN-HMMs trained using this method performed excellently in whisper speech recognition. [PERSON] et al. [10] used Mel-Frequency Cepstral Coefficients (MFCC) as features combined with DNNs to design a speech recognition and separation model, which achieved promising results in multi-speaker speech recognition. [PERSON] et al. [11] employed the DNN-HMM method, using the lower layers of the network to filter out noise and the upper layers to extract speech features, ultimately achieving good performance in smart home speech recognition. [PERSON] et al. [12] utilized a hybrid HMM/DNN model for computational paralinguistic tasks, proposing a new embedding extraction method that performed exceptionally well in handling variable-length speech segments and small-scale corpora. While the integration of deep learning in the aforementioned methods has led to improvements in model performance and accuracy, these systems still retain the original architecture, which includes multiple complex components such as acoustic models, language models, and pronunciation dictionaries. As a result, the training process remains cumbersome, with significant computational resource consumption. This does not fundamentally resolve the issues of structural complexity and computational burden, which continue to affect the accuracy and efficiency of speech recognition. With the emergence of end-to-end model architectures, speech recognition has advanced further. Unlike traditional structures, end-to-end methods directly convert raw audio into text output, with intermediate structures only designed as encoders and decoders. This approach eliminates several cumbersome processing steps, such as the construction of pronunciation dictionaries and the separation of acoustic and language models, thereby simplifying the model architecture. In recent years, end-to-end speech recognition, particularly research based on Transformer networks, has become a popular trend. [PERSON] et al. [13] employed the Espnet end-to-end architecture to recognize Mongolian speech and achieved satisfactory results. [PERSON] et al. [14] explored the enhancement of emotion recognition performance in their study on pre-trained models, utilizing Wav2 Vec 2.0 as the foundational framework. They investigated the optimization of low-level speech features and introduced improvements to input feature distribution and model architecture. The proposed approach demonstrated significant performance gains, particularly on small-scale or low-quality datasets. [PERSON] et al. [15] conducted a comparative study on the performance of Transformer and traditional RNN models in speech applications. Their research demonstrated that Transformers outperformed RNNs in speech recognition tasks, especially when handling long sentences and noisy environments. [PERSON] et al.