Abstract
The inherent shortcomings of the automatic translation system for spoken English are its lower accuracy and more errors. In order to improve the efficiency of automatic translation of spoken English, this paper builds an automatic translation model of spoken English based on improved machine learning algorithms based on improved machine learning algorithms. Moreover, on the basis of summarizing the signal transformation methods, feature extraction, and detection function generation methods of the existing note starting point detection algorithms, this paper proposes a note starting point detection framework inspired by the speech knowledge base, and proposes the partial note fluctuation characteristics under this framework. In addition, this paper extracts the characteristics of partial tone fluctuations to detect the starting point of the note, which can improve the performance of the detection algorithm. In addition, after the model is constructed, this paper designs experiments to analyze the performance of the model constructed in this paper and counts the research results. The research results show that the translation accuracy and translation speed of the model constructed in this paper can meet actual needs.
Published Version
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