Design of Intelligent Speech Translation System Based on Deep Learning

Abstract

In order to solve the problem of low translation accuracy caused by complex sentence parameters in traditional machine translation systems, a method based on deep learning was proposed. First, MCU SPCE061A is used to study the problem of complex digital signal. The training data in the synchronous translation server support the translation services of a large number of users, and the translation results were displayed through the session interface of the user terminal. The PMDL model is used to detect the keyword signal, record the PCM audio data, and slice the collected pulse code modulation signal, so as to wake up the artificial intelligence voice service. Then, this study establishes a speech recognition process that accurately outputs the speech-related semantics. In this paper, a manual interactive synchronous translation program is designed with the input text as the search criterion, and the set is trimmed to obtain the best translation effect. The experimental results show that the sentence translation accuracy of the system is 0.9 ∼ 1.0. It is proved that the method based on deep learning solves the problem of low accuracy of the traditional translation system.