Speech Recognition Model Inspired on Large Language Model for Smart Grid Dispatching

In recent years, large language models have gained popularity across various domains, with particular attention given to the impressive performance of their core component, the Transformer. This paper aims to enhance the accuracy of intelligent power grid dispatch speech recognition by leveraging deep learning techniques, specifically CNN and Transformer architectures. The proposed approach involves the creation of a specialized corpus tailored specifically for power dispatch speech recognition, focusing on power dispatch-specific terminology and regional grid dispatch language. The acoustic model training utilizes deep neural networks as the fundamental framework. Inspired by the success of Transformers in large language models, we incorporate Transformers as the language model to further enhance prediction performance. The practical results highlight the superiority of the Transformer-based power dispatch speech recognition compared to traditional speech recognition frameworks. With an impressive accuracy in power dispatch speech recognition, the developed system based on this approach has been successfully deployed and validated in a regional grid control center, affirming its feasibility and effectiveness.