English Speech Recognition System Based on Long Short Term Memory Algorithm

This paper discusses the development and execution of an English speech recognition system utilizing the Long Short-Term Memory (LSTM) algorithm. LSTM, a specific form of recurrent neural network (RNN), is extensively employed in deep learning due to its ability to manage long-term dependencies. It has found broad applications in speech recognition tasks. The system adopts an end-to-end architecture, starting from the front-end signal processing module, capturing the key features of audio signals using feature extraction techniques such as Mel Frequency Cepstral Coefficients (MFCCs), and then training the LSTM model on these features to achieve accurate transcription of the input speech. In the model training phase, a large dataset of English speech was used, including standard TIMIT pronunciation dictionaries, to ensure that the model could maintain good generalization ability under different accents and pronunciation conditions. Experimental results show that the error rate of the proposed LSTM-based speech recognition system is significantly lower than that of traditional HMM-based methods on various test sets, especially in handling recordings with background noise, demonstrating stronger robustness. Furthermore, the system also shows good real-time processing capabilities, which are expected to be widely applied in future intelligent voice assistants, telephone customer service systems, etc. However, the system still has certain limitations in handling non-standard English pronunciations and multi-language mixed scenarios, and future work will focus on solving these problems and further improving the system's flexibility and accuracy.