Construction of Foreign Language Translation Recognition System Model Based on Artificial Intelligence Algorithms

With the development of economic globalization, many foreign languages have become the most common languages in the world and have been increasingly applied. However, there is a shortage of personnel with high foreign language proficiency in various industries. People should strengthen the cultivation of language skills according to market needs. Translation is the core of language ability, and in order to apply foreign languages to practical communication activities, it is necessary to pay attention to the correction and research of foreign language pronunciation. Previously, foreign language translation systems were conducted according to the instructions of teachers, which not only consumed a lot of manpower but also made it difficult to implement. Therefore, it is very important to research and establish an intelligent translation system. This mode can monitor students' foreign language pronunciation and provide correct suggestions. The traditional intelligent recognition mode and settings for foreign language translations are not accurate enough to accurately recognize foreign language translations, correct foreign language pronunciation correctly, and even produce incorrect pronunciation, thereby affecting foreign language learning. Based on the above issues, this article studied an intelligent foreign language translation recognition model based on CNN (Convolutional Neural Networks) algorithm and DTW (Dynamic Time Warping) algorithm, in order to automatically recognize foreign language translations. In order to verify the effectiveness of the model, simulation experiments were conducted in the simulated environment in the article. The experimental results showed that compared to existing systems, the machine synchronous translation system of artificial intelligence technology and speech recognition (in this article) had the highest translation recognition accuracy of 95 00%.