Analysis of English Pronunciation Transformation Text Model based on Neural Network Pattern Recognition Algorithm

This study proposes an English pronunciation transformation text model based on a neural network pattern recognition algorithm, which aims to address various challenges in speech processing through in-depth analysis and innovative approaches. The paper first introduces the basic principles of pattern recognition, emphasizing the transition from feature space to decision space. Subsequently, a literature review highlights the transformative impact of convolutional neural networks (CNNs) in diverse domains, including financial markets, neuroimaging, environmental monitoring, and more. Methodologically, the paper describes a novel pattern recognition algorithm based on spiking neural networks. The algorithm overcomes the limitations of multi-layer network learning through the Spike Propagation (Spike Prop) algorithm, which introduces error back-propagation for effective training. Ensemble methods are used to improve the generalization ability of the system, with a focus on neural network integration. For pronunciation transformation in text models, sound features are extensively studied, including features such as short-term energy, short-term average amplitude, and Mel-frequency cepstrum coefficients. The experimental part, conducted in a Windows 10 environment using Python, TensorFlow, and GTX1660 GPU, demonstrates positive results across 30 experimental groups. The system shows significant improvements in character recognition accuracy, a reduction in missing and multiple word occurrences, a reduction in typos, and an increase in word accuracy. These results provide a solid foundation for the future research and practical applications in natural language processing.