Design and Implementation of Consecutive Interpreting System Based on Transformer NMT Model

Abstract

The traditional machine translation system with push-to-talk mode is not suitable for the processing of long-time oral translation. This paper proposed a consecutive interpreting system, solving the problem of long-time continuous listening by using pipeline work mode. In this mode, audio sampling is always on during the whole speech. In usage scenarios, audiences of the speeches or lectures can see bilingual subtitles on the projection or on their own device, and this system will keep translating while listens to the speaker. The speech-to-text module is based on the speech recognition model of Baidu open platform, and the translation is based on the Transformer NMT model proposed by Google. The average translation delay time of our system is only about 0.8s in our delay test. This system can play the role of the interpreter in conferences or lectures where translation precision requirement is not high.