As one of the most important communication tools for human beings, English pronunciation not only conveys literal information\nbut also conveys emotion through the change of tone. Based on the standard particle filtering algorithm, an improved auxiliary\ntraceless particle filtering algorithm is proposed. In importance sampling, based on the latest observation information, the\nunscented Kalman filter method is used to calculate each particle estimate to improve the accuracy of particle nonlinear\ntransformation estimation; during the resampling process, auxiliary factors are introduced to modify the particle weights to enrich\nthe diversity of particles and weaken particle degradation. The improved particle filter algorithm was used for online parameter\nidentification and compared with the standard particle filter algorithm, extended Kalman particle filter algorithm, and traceless\nparticle filter algorithm for parameter identification accuracy and calculation efficiency. The topic model is used to extract the\nsemantic space vector representation of English phonetic text and to sequentially predict the emotional information of different\nscales at the chapter level, paragraph level, and sentence level. The system has reasonable recognition ability for general speech,\nand the improved particle filter algorithm evaluation method is further used to optimize the defect of the English speech rationality\nand high recognition error rate Related experiments have verified the effectiveness of the method.
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