Research on Intelligent System of English Communication Aid Software Based on Speech Recognition

This paper aims to explore an innovative English communication aid software intelligent system that utilizes advanced speech recognition technology to improve the accuracy and efficiency of non-native speakers in English communication. The research focuses on the integration of feature parameter extraction methods, speech recognition models and human-computer interaction systems, as well as their application in practical communication scenarios. First, this study proposes an efficient feature parameter extraction method, which extracts key sound features such as Mayer frequency cepstrum coefficient (MFCCs) and linear predictive coding (LPCs) through in-depth analysis of speech signals, which are critical for subsequent speech recognition. By optimizing the extraction process of these parameters, the system can maintain good recognition performance under different background noise. Second, the study employs an advanced speech recognition model that combines deep learning techniques, especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to achieve accurate parsing of complex speech patterns. The model is trained with a large amount of spoken English data, and can effectively recognize English pronunciation with a variety of accents and speech speeds. In terms of human-computer interaction system, this research designs an intuitive and user-friendly interface that allows users to easily interact with the system. Through real-time feedback and automatic error correction, the system can assist users to correct pronunciation errors in real time, thereby improving the quality of communication. The experimental results show that the proposed system achieves the expected target in terms of recognition accuracy and response speed, and provides a powerful auxiliary tool for English learners and international communicators.