Russian Speech Conversion Algorithm Based on a Parallel Corpus and Machine Translation

Abstract

The phonetic conversion technology is crucial in the resource construction of Russian phonetic information processing. This paper explains how to build a corpus and the key algorithms that are used, as well as how to design auxiliary translation software and implement the key algorithms. This paper focuses on the “parallel corpus” method of problem solving and the indispensable role of a parallel corpus in Russian learning. This paper examines the foundations, motivations, and methods for using parallel corpora in translation instruction. The main way of using a parallel corpus in the classroom environment is to present data, so that learners can be exposed to a large amount of easily screened bilingual data, and translation skills and specific language item translation can be taught in a concentrated and focused manner. Among them, the creation of a large-scale Russian-Chinese parallel corpus will play an important role not only in improving the translation quality of Russian-Chinese machine translation systems but also in Chinese and Russian teaching as well as other branches of linguistics and translation studies, all of which should be given sufficient attention. This paper proposes the use of automatic speech analysis technology to assist Russian pronunciation learning and designs a Russian word pronunciation learning assistant system with demonstration, scoring, and feedback functions, in response to the shortcomings of pronunciation teaching in Russian teaching in China. It can provide corpus support for gathering a large number of parallel corpora and, in the future, enabling online translation. This system is used for corpus automatic construction, and future corpus automatic construction systems could be built on top of it. The proper application of parallel corpus data will aid in the development of a high-quality autonomous learning and translation teaching environment.