Construction of English Pronunciation Judgment and Detection Model Based on Deep Learning Neural Networks Data Stream Fusion.

Aiming at the defects of pronunciation errors and limited collection of pronunciation data resources in traditional artificial neural networks, an English pronunciation judgment and detection model based on deep learning neural networks data stream fusion is proposed. Taking Chinese English pronunciation as the research object, three groups of phonetic data were selected as experimental auxiliary data, based on the convolutional neural network, through the preset reset of the pronunciation detection system of the model, the sampling and recognition extraction of the speech system, the wrong speech detection and the feature analysis of the multi-level data stream tandem, the experiments are carried out with CU-CHLOE language learning database, WSJ1 database and 863 Mandarin database. The experimental results show that the recognition accuracy of this model is higher than that of the traditional neural network model, the accuracy of error type diagnosis is significantly improved, and its noise robustness is the best. [ABSTRACT FROM AUTHOR]