Generative and Discriminative Modelling of Linear Energy Sub-bands for Spoof Detection in Speaker Verification Systems

English translation, also a kind of language conversion, refers to the activities of expressing each other in English to express another language or another language to express English. This paper aimed to evaluate and research methods of English translation based on the improved Gaussian mixture model (GMM). The paper proposed a simple evaluation and analysis of English translation using a discriminative training algorithm. In the experimental part, on the one hand, taking students, teachers, and translators as case study objects, through a questionnaire survey, it can be known that the number of students who scored more than 80 points in the English translation test is increasing every year, from 11.30% in the first year to 21.13% in the fourth year, an increase of nearly 10%. On the other hand, it can be seen from the speech translation experiment that the correct translation rate was 46.18% by using intelligent technology to distinguish translation. The experimental results showed that the discriminative training algorithm under the improved GMM algorithm is effective in the research of English translation evaluation.

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Generative and Discriminative Modelling of Linear Energy Sub-bands for Spoof Detection in Speaker Verification Systems

Cited by 4 publications

References 30 publications

Noise Robust ASV Spoof Detection Using Integrated Features and Time Delay Neural Network

Noise Robust ASV Spoof Detection Using Integrated Features and Time Delay Neural Network

Complementary regional energy features for spoofed speech detection

Application of Discriminative Training Algorithm Based on the Improved Gaussian Mixture Model in English Translation Evaluation

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