Application and Performance Analysis of HMM Algorithm Based on DBSCAN in Simultaneous Interpretation of Translation Robots
With the increasing demand from society for the simultaneous interpretation function of English translation robots,the simultaneous interpretation function of many translation robots urgently needs to be improved.To address this issue,a density based clustering algorithm is studied to improve the hidden Markov model,and a new simultaneous interpretation model for translation robots is constructed based on the improved algorithm.Comparative experiments on the improved hidden Markov model algorithm showed that the highest accuracy and recall rates of the improved algorithm in the dataset were 0.913 and 0.912,respectively,which were significantly better than the comparison algorithm.Subsequently,an empirical analysis was conducted on the performance of the sim-ultaneous interpretation model,and the results showed that the proposed simultaneous interpretation model had a translation time and stability of 78.3 seconds and 0.79 seconds,respectively,significantly better than traditional models.The above results indicate that both the improved hidden Markov model algorithm proposed in the study and the simultaneous interpretation model based on this algo-rithm have excellent performance.Therefore,its application in English translation robot simultaneous interpretation can promote the development of the field of simultaneous interpretation.
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