In recent years,deep learning methods have achieved superior results compared to traditional machine learning methods in Automatic Speech Recognition (ASR).Particularly,deep learning methods based on Long Short-Term Memory (LSTM) have improved ASR performance.However,traditional LSTM models have limitations when it comes to handling continuous input streams,requiring substantial memory bandwidth and computational resources.This research introduces an enhanced deep learning model called LSTM Recurrent Neural Network (RNN),which addresses these limitations.In this model,the RNN serves as a “forgetting gate” integrated into the memory block.It allows for efficient processing of continuous input streams by resetting the cell state at the beginning of each subsequence.Additionally,modifications have been made to the standard architecture of the LSTM network to more effectively utilize model parameters.Compared to methods based on CNN and sequential models,the LSTM-RNN model achieves a 99.36% accuracy on the power grid dispatch speech dataset,surpassing other deep learning models.