Application of Discriminative Training Algorithm Based on Intelligent Computing in English Translation Evaluation

Abstract

Abstract Aiming at the higher correlation between the objective evaluation of computer English speech and the subjective evaluation of experts, an acoustic model based on discriminative training is proposed to improve the confidence score of objective evaluation. First, the process of obtaining the pronunciation quality evaluation score of the speech vector by the forced matching algorithm is introduced, and then the mathematical theory of hypothesis testing is used to prove that the acoustic model trained based on the discriminative algorithm ‘minimum phoneme error’ is more effective than the acoustic model based on the traditional maximum likelihood algorithm. Confidence scores close to subjective assessments are obtained. By calculating the correlation coefficient of the subjective and objective evaluation results, the experiment verifies that the speech evaluation system using the discriminative acoustic model can give a higher confidence score and proposes a data selection method based on dynamic weighting, which is applied to continuous speech recognition in the discriminative training of the acoustic model. This method combines the posterior probability and the phoneme accuracy rate to select the data. First, the Beam algorithm of the posterior probability is used to trim the word graph. On this basis, according to the error rate of the candidate path where the candidate word is located, the probability dynamically assigns different weights to the candidate words; second, by calculating the confusion degree between the phoneme pairs, different penalty weights is dynamically added to the easily confused phoneme pairs to calculate the phoneme accuracy; finally, the expected accuracy of the obtained arc is calculated on the basis of the probability distribution. The Gaussian function is used to softly weight the expected phoneme accuracy of all competing arcs. The experimental results show that compared with the minimum phoneme error criterion, the dynamic weighting method has higher recognition accuracy and can effectively reduce the training time.