This research aims to explore the integration method of deep learning and large language models in speech recognition to improve the system’s recognition accuracy and ability to handle complex contexts. Deep neural network (DNN), convolutional neural network (CNN), long short-term memory network (LSTM) and Transformer-based large language model are used to build an integrated acoustic and language model framework. Experiments on TIMIT, LibriSpeech and Common Voice datasets show that the ensemble model shows significant improvements in both word error rate (WER) and real-time factor (RTF) compared to traditional models. Especially in terms of adaptability to multiple languages and accent changes, the model shows superior performance. The conclusion shows that through technology integration, the performance of the speech recognition system in complex environments can be effectively improved, providing a new direction for the future development of speech recognition technology.

Generative Adversarial Networks (GANs) have significantly advanced image processing, with Pix2Pix being a notable framework for image-to-image translation. This paper explores a novel application of Pix2Pix to transform abstract map images into realistic ground truth images, addressing the scarcity of such images crucial for domains like urban planning and autonomous vehicle training. We detail the Pix2Pix model's utilization for generating high-fidelity datasets, supported by a dataset of paired map and aerial images, and enhanced by a tailored training regimen. The results demonstrate the model's capability to accurately render complex urban features, establishing its efficacy and potential for broad real-world applications.

Faced with the challenges of accelerating growth in unstructured text data and increasing risk concealment in the financial market, this study constructs a financial risk assessment system that combines text mining with large language models (LLMs). This system forms an end-to-end architecture encompassing data, knowledge, models, services, and governance. The system collects multi source text, constructs a risk knowledge graph, and extracts key events and sentiment signals. These are then integrated into the LLM framework of retrieval augmented generation (RAG) and multi feature fusion to achieve credit risk prediction and default probability estimation. This experiment relies on the FinBen Lending Club dataset (2024) and conducts comparative experiments, ablation studies, error analysis, and stability tests. The model outperforms traditional structured models and plain text models in key evaluation indicators such as F1, MCC, and PR AUC. In scenarios of market environment changes, cross industry migration, and anti-interference, the model’s stability and compliance performance are outstanding. This study designs an intelligent risk identification solution for financial institutions, which makes the identification process explainable, traceable, and auditable. This study has significant theoretical and practical impact on risk governance and decision support for banks, securities, insurance, and regulatory authorities.