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.

中文翻译：

---

基于深度学习的智能语音翻译系统设计

为了解决传统机器翻译系统中句子参数复杂导致翻译准确率低的问题，提出了一种基于深度学习的方法。首先，单片机SPCE061A用于研究复杂数字信号的问题。同步翻译服务器中的训练数据支持大量用户的翻译服务，翻译结果通过用户终端的会话界面进行展示。PMDL模型用于检测关键词信号，记录PCM音频数据，对采集到的脉冲编码调制信号进行切片，从而唤醒人工智能语音服务。然后，本研究建立了一个准确输出语音相关语义的语音识别过程。在本文中，设计了一个手动交互同步翻译程序，以输入文本为搜索标准，对集合进行修剪以获得最佳翻译效果。实验结果表明，该系统的句子翻译准确率为0.9∼1.0。实践证明，基于深度学习的方法解决了传统翻译系统准确率低的问题。