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

Authors: Yingyi Zhang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## 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.


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## Body

## 1. Introduction

With the deepening of globalization, the deepening of China’s opening to the outside world, the increasing frequency of foreign exchanges and cooperation in various industries, and the increasing demand for information localization, it has brought unprecedented market opportunities to China’s translation service providers, making translation, as an emerging industry in China, grow up at a rapid speed [1]. A corpus is a collection of standardized original corpora with a specific scale and structure that can be retrieved by computer programs and that have been collected and processed specifically for one or more application goals. It is split into parallel and comparable corpora [2]. The use of computers to automatically mark phonetics for words and convert word text spelled by letters into word pronunciation that can be read and processed by people or machines is referred to as word to sound conversion. Russian word sound conversion technology can help build a Russian pronunciation dictionary and solve the problem of automatic phonetic annotation of foreign words [3]. Several cross-language information retrieval systems have emerged in recent years. Question translation is used to implement the majority of these systems. The main idea behind question translation is to first translate the user’s query statements into all of the system’s supported language statements, then perform information retrieval for different languages, and finally synthesize and return the retrieval results to the user [4]. The establishment of a parallel corpus has a history of more than 20 years abroad, and its application in teaching has been studied for more than 10 years. In China, the application of a parallel corpus in teaching starts late. Generally speaking, the research on the application of a corpus in translation teaching lags behind that of corpus translatology. It is urgent to develop effective application methods to expand its application in translation [5].With the continuous development of international communication, people have increasingly realized the importance of learning and using foreign languages. The teaching of non-native language has become a hot spot in the field of current education and teaching. As one of the richest languages in the world, Russian has received far more attention in China than other languages except English due to historical and geographical reasons. Russian pronunciation conversion based on rules is achieved with the help of a Russian accent dictionary during the development of a Russian speech recognition system, according to Russian consonant change and vowel weakening rules. The volume of translation is enormous, far exceeding the capabilities of pure manual labor, and terminology and translation must be consistent. It not only raises new questions about the feasibility and in-depth research of machine translation and computer-assisted translation but also creates a significant market for the latter [6]. In this sense, the industrialization of translation is inextricably linked to information technology, and the advancement of information technology has aided in the formation of a large translation market, which has become the reason for the existence of large translation service enterprises [7]. Cross-language information retrieval research enables all types of information in a mutual network to be combined with each other, breaks down language barriers, improves users’ ability to acquire information, and increases communication and information sharing among users of different languages [8]. A parallel corpus is a text corpus composed of translated texts, which is an important resource in the field of natural language processing such as machine translation, cross-language information retrieval, and dictionary construction. Research on efficient, large-scale, multidomain, and sustainable development of a parallel resource database is of great significance to natural language processing.Using a corpus in teaching can obtain a corpus more conveniently. Relevant studies have shown that a corpus can enable learners to directly obtain unlimited language data, and the role of a corpus and corpus tools in translation teaching is immeasurable [9]. There are two main ways to use corpus data: corpus driven and corpus based. The former is consistent with discovery learning. The latter can provide documentary evidence and provide assistance and reference for the translation process. Sino-Russian relations have developed well in recent years. The market in European and American countries has begun to shrink. More and more enterprises hope to turn the market to Russia [10]. However, the shortage of Russian translation talents and the low quality of Russian-Chinese machine translation have added great difficulties to the development of the Russian market. Cultivating Russian translation talent is important, but it is a long-term process that requires a lot of money and will not solve the problem right away. As a result, one of the main development directions to solve this problem is the development and improvement of a Russian-Chinese machine translation system [11]. This paper discusses the feasibility of establishing a parallel corpus in theory and practice, which is not only beneficial to the development of Chinese character sound conversion in Russia, but also plays an important role in Chinese and Russian teaching, as well as other branches of linguistics and translation. In translation classes, learners’ initiative is very important. Using the parallel corpus as a platform, the retrieval and extraction functions can allow students to participate in the selection of teaching content and translation materials, as well as provide classroom discussion and group activities to increase participation awareness [12]. In addition, parallel corpora, regardless of translation methods or translators, aid in the development of students’ rigorous work style and critical thinking ability. This is what learner-centered translation instruction looks like.


## 2. Related Work

Reference [13] proposed a phonetic conversion method based on an expectation maximization (EM) algorithm to achieve one-to-one alignment of morpheme and phoneme and to establish pronunciation model by N-gram. In Reference, using the statistical method of frequency data to carry out translation language research, the method of obtaining data is retrieval, which means that the corpus used in translation teaching also depends on frequency and retrieval. In Reference [14], through the construction of the “Chinese-Russian parallel corpus,” it is found that this corpus is conducive to solving these problems. In other words, the “Chinese-Russian parallel corpus” plays an irreplaceable role in Russian learning. Reference [15] puts forward that the phonetic conversion methods can be divided into rule-based methods and data-driven methods. The rule-based method is to make Russian pronunciation rules manually by summarizing Russian orthography and pronunciation rules, so as to predict the pronunciation of words. Reference [16] points out that the society urgently needs large-scale processing of real texts, and the machine translation system is far from the expectation of large-scale processing of real texts in today’s society. In this context, the concept of machine-assisted translation was born. In light of this, this paper investigates the automatic construction of large-scale bilingual parallel corpora based on template transformation, with the goal of automatically acquiring a large number of parallel texts from the Internet without the need for human intervention. According to Reference [17], computers do not consider the semantic and pragmatic functions of sentences during machine translation, instead focusing on the syntactic components of sentences. Translators can be provided with a set of possible translation units or equivalent text units in different languages using bilingual corpus data, allowing them to gain a new understanding of how to deal with translation difficulties and thus improve the translation. Translators can be provided with a set of possible translation units or equivalent text units in different languages using bilingual corpus data, allowing them to gain a new understanding of how to deal with translation difficulties and thus improve the translation.Reference [18] defines corpus linguistics as the study of language according to text materials. The interpretation of this term in literature is a language research based on examples of language use in real life. Literature [19] holds that corpus linguistics is the starting point of language description or the method of verifying language hypotheses based on a corpus. Reference [20] realizes the mining of candidate parallel texts through cross language information retrieval technology, and users use one language to retrieve the required information from the data set expressed in another or more languages. This process takes each Chinese text as a query to retrieve similar text, and finally get a similar text list. Reference [21] proposed that the parallel corpus is a collection of texts corresponding to different languages, and the language directions of its translation are diverse. A data-driven word to sound conversion algorithm has been studied at home and abroad, but the application object is mainly English, and there is no relevant research and experiment in Russian. Therefore, based on the knowledge of Russian phonetics, it is necessary to improve the Russian corpus resources and further study the implementation and application of the Russian word sound conversion algorithm. According to the research of literature [22], a bilingual corpus plays an active role in translation teaching. It is reflected in the following two aspects: (1) the presentation of the corresponding corpus can improve students’ awareness of translation skills and the improvement of their understanding of translation skills will help to promote students’ correct use of translation skills and improve translation quality, and (2) skill understanding is not directly related to translation quality, indicating that the presentation of a corpus does not necessarily form a meaningful generalization.


## 3. Methodology

### 3.1. EM Algorithm

According to the pronunciation characteristics of Russian words, this paper adopts the “many to many” alignment method of Russian words and sounds based on the EM algorithm, then trains and transforms the alignment results into a WFST pronunciation model by using the joint N-gram model, and finally decodes them by the shortest path algorithm to realize the Russian word and sound conversion based on WFST. Among them, the key algorithms used to build a multilingual parallel corpus are emphasized, such as the same language text similarity algorithm and cross-language text similarity algorithm. A joint EM optimization method is used to jointly learn the translation models from source language to target language and target language to source language. In the whole training process, starting from these two unidirectional weak translation models (those with poor initial performance), a small-scale bilingual corpus is used for initial pretraining, and the two models are iteratively updated by gradually reducing the translation loss of training data. When there are hidden variables or missing data in a given sample, the expectation maximization algorithm can be used to solve the maximum likelihood estimation of model parameters and realize probability modeling. Maximizing the logarithmic conditional probability of correct translation satisfies the following formula:(1)θ∗=argmaxθ∑i=1N∑j=1tilogpti,jti,0⟶j−1,si.Therefore, the translation probability meets the following conditions:(2)psti≈qs.The implicit variable of this problem is the possible alignment result. The expectation maximization algorithm is divided into two steps. The first step is the expectation calculation process, and the second step is the process of solving the maximization. The flow of Russian word sound conversion and test method based on the EM algorithm is shown in Figure1.Figure 1
Flow chart of Russian word sound conversion and test method based on the EM algorithm.The EM algorithm first traverses each subsequence of words and sounds, generates a finite state receiver word graph according to each alignment combination, then calculates the expected values of all alignments, and finally obtains the alignment results that maximize the expectation. The deformation coefficient is(3)SimDi,Dj=2∗InnerClen+Elen.In the pretraining process of the model, firstly, the bilingual parallel corpus is preprocessed, including the preprocessing of source language and target language. The processed corpus is aligned and a bilingual dictionary is generated. The bilingual dictionary is used to complete the aligned translation of the source language and the target language to achieve the purpose of model training. The pretraining process is completed by the traditional method based on the maximum likelihood principle. The pretraining process is completed by the traditional method based on the maximum likelihood principle. The N-gram language model is a statistical language model widely used in natural language processing, and it has been successfully used in statistics-based speech recognition, machine translation, Chinese word segmentation, and other applications. This model is simple and flexible, especially suitable for serial data. Therefore, the N-gram model can be used to similarly establish pronunciation models for joint phonetic phoneme sequences. The joint calculation formula of the sequence is as follows:(4)Pγ,π=∑a∈Aγ,πQ′a.A joint EM training algorithm is used to optimize the two translation models to improve their translation performance. The pronunciation model is constructed by training the joint morpheme and phoneme sequence with the N-gram language model and obtaining the probability parameters of the phoneme sequence. The phoneme recognition error rate of the standard phoneme model is shown in Figure2.Figure 2
Phoneme recognition error rate of the standard phoneme model.The average value of the logarithm posterior probability of phoneme segments in sentences normalized by phoneme length is(5)p=1N∑i=1Npidi.The N-gram model is then transformed into a weighted finite state converter to create the decoder’s search space. The memory-based machine aided translation engine is regarded as a full-text retrieval search engine that incorporates relevant information retrieval and assisted translation methods and technologies. This paper’s theoretical foundation and breakthrough is the inverted document index structure in full-text index, which is used to significantly improve retrieval efficiency.


### 3.2. Construction of the Parallel Corpus System

Since machine aided translation software requires a large number of corpora, it is necessary to build a high-quality and large number of multilingual parallel corpora. The bilingual text verification method based on the number of conversion modes does not reflect the difference in the ranking of search results, and the accuracy rate, an important index to evaluate the performance of the retrieval system, uses the ranking value of the file list returned by the danger search system. In order to improve candidate text verification This paper proposes bilingual text verification based on transformation pattern retrieval and sorting. Set the prediction score of conversion modeT as
(6)FT=∑Sij1RankSij−θ.The workflow of building a corpus is divided into: (1) file preprocessing, including deleting duplicate text files and file format conversion; (2) file warehousing, i.e., saving files to the database; (3) file alignment, cross language file alignment, and determining the alignment relationship between source language files and target language files; (4) document cutting and sentence alignment; and (5) extracting useful vocabulary from aligned sentences to form a pair of translated vocabulary. A corpus-based method first needs to build a Russian parallel corpus, and the corpus in the corpus is also marked with parts of speech. This system is a WEB parallel corpus construction system based on the Internet, which is developed in B/S mode. The whole system mainly consists of two subsystems, namely, crawler system and index grading system. The subsystems are loosely coupled and have no influence on each other at runtime. The language configuration in the system is flexible, that is to say, although the system is initially set as a bilingual corpus, after completion, the system can be further upgraded and maintained, so that parallel corpora of any two languages can be configured and constructed. Although the algorithm of sentence similarity should be used in corpus alignment, because of the characteristics of a corpus of a Chinese translation company, that is, the corpus alignment is very neat, the method of sentence similarity is not adopted, but only two articles that have already been aligned are segmented, displayed after clauses, and provided to editors for alignment. Although there is no algorithm involved, because this tool needs to be used for a long time, its interface design and function design are very critical, and it is necessary to provide friendly interface and reasonable functions. The error rate of phoneme recognition without the adaptive HMM model is shown in Figure3.Figure 3
Phoneme recognition error rate without the adaptive HMM model.The phoneme recognition error rate of the adaptive HMM model is shown in Figure4.Figure 4
Phoneme recognition error rate of the adaptive HMM model.The combination of massive data and deep learning technology gives birth to neural machine translation, which can achieve very good results in languages with a rich corpus. However, machine translation systems with good performance, including statistical machine translation system and neural machine translation system, seriously rely on a large number of parallel corpora. The recognition ability of the neural network to confusing phonemes is shown in Figure5.Figure 5
Comparison of recognition results of confusing phonemes by HMM and MLP.According to the number of languages, corpora can be divided into monolingual corpora, bilingual corpora and multilingual corpora. According to the content of the corpus, it can be divided into an original language corpus and a translation language corpus. According to language users, it can be divided into native speakers’ language corpus and learners’ foreign language corpus. According to the existing form of language, it can be divided into an oral corpus and a written corpus. According to different methods, corpora can be divided into different categories. Among them, the most closely related to machine translation is the bilingual or multilingual parallel corpus, that is, the bilingual or multilingual corpus composed of the original text and its parallel corresponding target text. The two subsystems rely on the database as the connection, so it can also be said that the core of the whole system is around the database. Many places rely on the database. The crawler is not only the core part of the search engine, but also the core part of building a parallel corpus in this paper. The functions of the crawler mainly include web page recognition, that is, first grab the required web pages through the web crawler, compare and classify the information through a series of feature judgments, and consider whether to process the web pages in the next step. After the web pages are recognized as bilingual parallel pages, purify the web pages, filter out the unnecessary information, and finally generate a corpus.


## 3.1. EM Algorithm

According to the pronunciation characteristics of Russian words, this paper adopts the “many to many” alignment method of Russian words and sounds based on the EM algorithm, then trains and transforms the alignment results into a WFST pronunciation model by using the joint N-gram model, and finally decodes them by the shortest path algorithm to realize the Russian word and sound conversion based on WFST. Among them, the key algorithms used to build a multilingual parallel corpus are emphasized, such as the same language text similarity algorithm and cross-language text similarity algorithm. A joint EM optimization method is used to jointly learn the translation models from source language to target language and target language to source language. In the whole training process, starting from these two unidirectional weak translation models (those with poor initial performance), a small-scale bilingual corpus is used for initial pretraining, and the two models are iteratively updated by gradually reducing the translation loss of training data. When there are hidden variables or missing data in a given sample, the expectation maximization algorithm can be used to solve the maximum likelihood estimation of model parameters and realize probability modeling. Maximizing the logarithmic conditional probability of correct translation satisfies the following formula:(1)θ∗=argmaxθ∑i=1N∑j=1tilogpti,jti,0⟶j−1,si.Therefore, the translation probability meets the following conditions:(2)psti≈qs.The implicit variable of this problem is the possible alignment result. The expectation maximization algorithm is divided into two steps. The first step is the expectation calculation process, and the second step is the process of solving the maximization. The flow of Russian word sound conversion and test method based on the EM algorithm is shown in Figure1.Figure 1
Flow chart of Russian word sound conversion and test method based on the EM algorithm.The EM algorithm first traverses each subsequence of words and sounds, generates a finite state receiver word graph according to each alignment combination, then calculates the expected values of all alignments, and finally obtains the alignment results that maximize the expectation. The deformation coefficient is(3)SimDi,Dj=2∗InnerClen+Elen.In the pretraining process of the model, firstly, the bilingual parallel corpus is preprocessed, including the preprocessing of source language and target language. The processed corpus is aligned and a bilingual dictionary is generated. The bilingual dictionary is used to complete the aligned translation of the source language and the target language to achieve the purpose of model training. The pretraining process is completed by the traditional method based on the maximum likelihood principle. The pretraining process is completed by the traditional method based on the maximum likelihood principle. The N-gram language model is a statistical language model widely used in natural language processing, and it has been successfully used in statistics-based speech recognition, machine translation, Chinese word segmentation, and other applications. This model is simple and flexible, especially suitable for serial data. Therefore, the N-gram model can be used to similarly establish pronunciation models for joint phonetic phoneme sequences. The joint calculation formula of the sequence is as follows:(4)Pγ,π=∑a∈Aγ,πQ′a.A joint EM training algorithm is used to optimize the two translation models to improve their translation performance. The pronunciation model is constructed by training the joint morpheme and phoneme sequence with the N-gram language model and obtaining the probability parameters of the phoneme sequence. The phoneme recognition error rate of the standard phoneme model is shown in Figure2.Figure 2
Phoneme recognition error rate of the standard phoneme model.The average value of the logarithm posterior probability of phoneme segments in sentences normalized by phoneme length is(5)p=1N∑i=1Npidi.The N-gram model is then transformed into a weighted finite state converter to create the decoder’s search space. The memory-based machine aided translation engine is regarded as a full-text retrieval search engine that incorporates relevant information retrieval and assisted translation methods and technologies. This paper’s theoretical foundation and breakthrough is the inverted document index structure in full-text index, which is used to significantly improve retrieval efficiency.


## 3.2. Construction of the Parallel Corpus System

Since machine aided translation software requires a large number of corpora, it is necessary to build a high-quality and large number of multilingual parallel corpora. The bilingual text verification method based on the number of conversion modes does not reflect the difference in the ranking of search results, and the accuracy rate, an important index to evaluate the performance of the retrieval system, uses the ranking value of the file list returned by the danger search system. In order to improve candidate text verification This paper proposes bilingual text verification based on transformation pattern retrieval and sorting. Set the prediction score of conversion modeT as
(6)FT=∑Sij1RankSij−θ.The workflow of building a corpus is divided into: (1) file preprocessing, including deleting duplicate text files and file format conversion; (2) file warehousing, i.e., saving files to the database; (3) file alignment, cross language file alignment, and determining the alignment relationship between source language files and target language files; (4) document cutting and sentence alignment; and (5) extracting useful vocabulary from aligned sentences to form a pair of translated vocabulary. A corpus-based method first needs to build a Russian parallel corpus, and the corpus in the corpus is also marked with parts of speech. This system is a WEB parallel corpus construction system based on the Internet, which is developed in B/S mode. The whole system mainly consists of two subsystems, namely, crawler system and index grading system. The subsystems are loosely coupled and have no influence on each other at runtime. The language configuration in the system is flexible, that is to say, although the system is initially set as a bilingual corpus, after completion, the system can be further upgraded and maintained, so that parallel corpora of any two languages can be configured and constructed. Although the algorithm of sentence similarity should be used in corpus alignment, because of the characteristics of a corpus of a Chinese translation company, that is, the corpus alignment is very neat, the method of sentence similarity is not adopted, but only two articles that have already been aligned are segmented, displayed after clauses, and provided to editors for alignment. Although there is no algorithm involved, because this tool needs to be used for a long time, its interface design and function design are very critical, and it is necessary to provide friendly interface and reasonable functions. The error rate of phoneme recognition without the adaptive HMM model is shown in Figure3.Figure 3
Phoneme recognition error rate without the adaptive HMM model.The phoneme recognition error rate of the adaptive HMM model is shown in Figure4.Figure 4
Phoneme recognition error rate of the adaptive HMM model.The combination of massive data and deep learning technology gives birth to neural machine translation, which can achieve very good results in languages with a rich corpus. However, machine translation systems with good performance, including statistical machine translation system and neural machine translation system, seriously rely on a large number of parallel corpora. The recognition ability of the neural network to confusing phonemes is shown in Figure5.Figure 5
Comparison of recognition results of confusing phonemes by HMM and MLP.According to the number of languages, corpora can be divided into monolingual corpora, bilingual corpora and multilingual corpora. According to the content of the corpus, it can be divided into an original language corpus and a translation language corpus. According to language users, it can be divided into native speakers’ language corpus and learners’ foreign language corpus. According to the existing form of language, it can be divided into an oral corpus and a written corpus. According to different methods, corpora can be divided into different categories. Among them, the most closely related to machine translation is the bilingual or multilingual parallel corpus, that is, the bilingual or multilingual corpus composed of the original text and its parallel corresponding target text. The two subsystems rely on the database as the connection, so it can also be said that the core of the whole system is around the database. Many places rely on the database. The crawler is not only the core part of the search engine, but also the core part of building a parallel corpus in this paper. The functions of the crawler mainly include web page recognition, that is, first grab the required web pages through the web crawler, compare and classify the information through a series of feature judgments, and consider whether to process the web pages in the next step. After the web pages are recognized as bilingual parallel pages, purify the web pages, filter out the unnecessary information, and finally generate a corpus.


## 4. Result Analysis and Discussion

### 4.1. The Function of a Parallel Corpus

Parallel corpora can be regarded as a sharp weapon in translation studies. It involves both the original text and the translation, and can reflect the corresponding relationship between the two. A parallel corpus, also known as “corresponding corpus,” is an important corpus divided according to the number of languages it contains. The establishment of the Russian parallel corpus is of great significance to the study of Russian-Chinese machine translation. The function of the parallel corpus is mainly reflected in the following aspects. (1) Verify the rules formulated in the machine translation system. The establishment of the parallel corpus can not only save time and energy for linguists to conduct effective research, but also test their research results, thus killing two birds with one stone. It is conducive to accurately grasping the Russian corresponding forms of Chinese idioms. The Chinese-Russian parallel corpus is rich in corpus, including clothes, food, housing, transportation and other aspects that are often used in daily life. Not only that, the parallel corpus also includes idioms and common sayings that are often used in daily life. Therefore, we should skillfully use the extremely high computing speed of computers to help linguists conduct translation studies and summarize the undiscovered laws of linguistics and translatology through parallel corpora. (2) It can help the machine translation system to find the original text and translation outside the rules by itself, and improve the quality of the translation of the machine translation system through analysis and imitation. The Chinese-Russian parallel corpus is a useful tool for students to compensate for this shortcoming. Translation, by its very nature, is based on practice, and translation teaching is no different, emphasizing the importance of accumulating experience. The bilingual parallel library’s indirect translation knowledge can clearly assist students in gaining translation experience. The machine translation system has the ability to self-correct thanks to a large corpus, and the final translation will be more in line with Chinese language habits.The establishment of the Russian-Chinese parallel corpus not only has a far-reaching impact on the research of Russian-Chinese machine translation, but also can be applied in other aspects. The establishment of the Russian-Chinese parallel corpus will be very conducive to the development of Russian-Chinese comparative linguistics. An effective and fast way to learn and master the Russian expression of Chinese neologisms is to use the Chinese-Russian parallel corpus. In addition, the establishment of the Russian-Chinese parallel corpus can also help to further develop translation theory, guide translation practice and translation teaching, reveal language commonalities, find translation language features, study a translator’s style, and so on. The use of bilingual parallel corpora in translation teaching has the following advantages: (1) it helps students find the complexity of language use and sentence patterns; (2) it is better for students to read examples in real texts than to rely on isolated examples in grammar books and textbooks; and (3) the corresponding corpus can be used as an expert system to turn learners’ attention to the typical (or atypical) processing methods of typical problems found by mature translators or expert translators. There are two main Russian corpora in Russia. The Chinese translation of a large number of Russian classic literary works and modern and contemporary novels provides a material basis for the establishment of the Russian-Chinese parallel corpus. They are Moscow University Newspaper text corpus (20th century Russian newspaper text computer corpus and Russian National Corpus). The relatively developed computer storage system and computer computing ability in China provide technical support for the establishment of the Russian-Chinese parallel corpus. Translators and computer designers provide spiritual power for the establishment of the Russian-Chinese parallel corpus. It can be said that the establishment of the Russian-Chinese parallel corpus is imminent.


### 4.2. System Research and Analysis

The basic principle of the system is to collect web resources through crawler analysis and store the processed information in the database. The main purpose of the Russian-Chinese parallel corpus discussed in this paper is to serve the Russian-Chinese machine translation research and provide corpus support for the Russian-Chinese machine translation system. Therefore, the collection scope of a corpus should be selected according to the directionality of the machine translation system. At this time, the data is only records related by the primary key of the database, which can already be called corpus. Then, by indexing the database with indexing system, it can be obtained as a parallel corpus. It is also necessary to master the expressions of new words to enhance the understanding of modern Russian and Russian culture and present situation. The technology used in this system is not speech recognition technology, which does not recognize the meaning of learners’ pronunciation, but speech analysis technology, which analyzes and judges the similarity between learners’ pronunciation and standard pronunciation stored in the system and gives scoring feedback. Here, a unified threshold is adopted for all phonemes in the system, and then the value of the threshold is adjusted to observe its influence on the correct rejection rate and correct acceptance rate of the system. The test results are shown in Figure6.Figure 6
Effect of threshold adjustment on system performance.In the process of system design and development, we should first ensure that the database has been successfully connected, and correctly set the maximum number of threads and the size of thread pool when the crawler runs, so as to ensure the correct operation of the crawler of the whole system. In addition, the response speed of the crawler system should meet the real-time requirements and feedback information in real time, so that the processing capacity and response time of the crawler can meet the requirements of users. The basic framework of the system is shown in Figure7.Figure 7
System framework.The design of this system aims to guide learners to learn the pronunciation of Russian words. It belongs to isolated word analysis in speech analysis technology. Compared with the analysis of the whole sentence, isolated word analysis technology is more mature, which increases the feasibility of the system. During the development of the system, full consideration should be given to the subsequent expansion of online translation function. To achieve this, it is necessary to make the system an open system, comply with certain development specifications, and simply add and reduce the system modules and configure the system hardware. Upgrade and update the system through the increase, decrease and modification of software modules.This system can further realize the online translation function by establishing a Chinese-English parallel corpus. The most common use of an entity relationship diagram is as a communication tool and bridge between database designers and database users during the analysis stage of database design. Entity relationship diagrams are used to create a conceptual data model, which is a representation of database structure that has nothing to do with database management systems or data models. This system is made up of three main components. After providing the tools for corpus construction, the crawler entity, message entity, and parallel webpage entity must develop data collection rules to ensure that the corpus and vocabulary pairs are useful and that the collected data meets specific requirements. Not only can corpus be used for linguistics research, but also it can be used to aid translation software. What kind of corpus pairs and vocabulary pairs can be provided to meet the requirements of the auxiliary translation software, all of which need to be constantly explored in the process of practice. At present, the rules formulated are the general outline, and the corpus extracted according to these rules may be further processed in the future, so as to be better provided for linguistic research and auxiliary translation tools. In the process of corpus collection and selection, on the one hand, linguists need to make unremitting efforts to widely collect corpus sources, investigate the standardization of translation and decide whether to be included in the database. On the other hand, it also needs technical support to make this process more high-quality and efficient. In the stage of corpus collection, we should pay attention to the proportion of different types of corpus, which meets the sampling standard; that is, the selection of a corpus should be comprehensive and representative.


## 4.1. The Function of a Parallel Corpus

Parallel corpora can be regarded as a sharp weapon in translation studies. It involves both the original text and the translation, and can reflect the corresponding relationship between the two. A parallel corpus, also known as “corresponding corpus,” is an important corpus divided according to the number of languages it contains. The establishment of the Russian parallel corpus is of great significance to the study of Russian-Chinese machine translation. The function of the parallel corpus is mainly reflected in the following aspects. (1) Verify the rules formulated in the machine translation system. The establishment of the parallel corpus can not only save time and energy for linguists to conduct effective research, but also test their research results, thus killing two birds with one stone. It is conducive to accurately grasping the Russian corresponding forms of Chinese idioms. The Chinese-Russian parallel corpus is rich in corpus, including clothes, food, housing, transportation and other aspects that are often used in daily life. Not only that, the parallel corpus also includes idioms and common sayings that are often used in daily life. Therefore, we should skillfully use the extremely high computing speed of computers to help linguists conduct translation studies and summarize the undiscovered laws of linguistics and translatology through parallel corpora. (2) It can help the machine translation system to find the original text and translation outside the rules by itself, and improve the quality of the translation of the machine translation system through analysis and imitation. The Chinese-Russian parallel corpus is a useful tool for students to compensate for this shortcoming. Translation, by its very nature, is based on practice, and translation teaching is no different, emphasizing the importance of accumulating experience. The bilingual parallel library’s indirect translation knowledge can clearly assist students in gaining translation experience. The machine translation system has the ability to self-correct thanks to a large corpus, and the final translation will be more in line with Chinese language habits.The establishment of the Russian-Chinese parallel corpus not only has a far-reaching impact on the research of Russian-Chinese machine translation, but also can be applied in other aspects. The establishment of the Russian-Chinese parallel corpus will be very conducive to the development of Russian-Chinese comparative linguistics. An effective and fast way to learn and master the Russian expression of Chinese neologisms is to use the Chinese-Russian parallel corpus. In addition, the establishment of the Russian-Chinese parallel corpus can also help to further develop translation theory, guide translation practice and translation teaching, reveal language commonalities, find translation language features, study a translator’s style, and so on. The use of bilingual parallel corpora in translation teaching has the following advantages: (1) it helps students find the complexity of language use and sentence patterns; (2) it is better for students to read examples in real texts than to rely on isolated examples in grammar books and textbooks; and (3) the corresponding corpus can be used as an expert system to turn learners’ attention to the typical (or atypical) processing methods of typical problems found by mature translators or expert translators. There are two main Russian corpora in Russia. The Chinese translation of a large number of Russian classic literary works and modern and contemporary novels provides a material basis for the establishment of the Russian-Chinese parallel corpus. They are Moscow University Newspaper text corpus (20th century Russian newspaper text computer corpus and Russian National Corpus). The relatively developed computer storage system and computer computing ability in China provide technical support for the establishment of the Russian-Chinese parallel corpus. Translators and computer designers provide spiritual power for the establishment of the Russian-Chinese parallel corpus. It can be said that the establishment of the Russian-Chinese parallel corpus is imminent.


## 4.2. System Research and Analysis

The basic principle of the system is to collect web resources through crawler analysis and store the processed information in the database. The main purpose of the Russian-Chinese parallel corpus discussed in this paper is to serve the Russian-Chinese machine translation research and provide corpus support for the Russian-Chinese machine translation system. Therefore, the collection scope of a corpus should be selected according to the directionality of the machine translation system. At this time, the data is only records related by the primary key of the database, which can already be called corpus. Then, by indexing the database with indexing system, it can be obtained as a parallel corpus. It is also necessary to master the expressions of new words to enhance the understanding of modern Russian and Russian culture and present situation. The technology used in this system is not speech recognition technology, which does not recognize the meaning of learners’ pronunciation, but speech analysis technology, which analyzes and judges the similarity between learners’ pronunciation and standard pronunciation stored in the system and gives scoring feedback. Here, a unified threshold is adopted for all phonemes in the system, and then the value of the threshold is adjusted to observe its influence on the correct rejection rate and correct acceptance rate of the system. The test results are shown in Figure6.Figure 6
Effect of threshold adjustment on system performance.In the process of system design and development, we should first ensure that the database has been successfully connected, and correctly set the maximum number of threads and the size of thread pool when the crawler runs, so as to ensure the correct operation of the crawler of the whole system. In addition, the response speed of the crawler system should meet the real-time requirements and feedback information in real time, so that the processing capacity and response time of the crawler can meet the requirements of users. The basic framework of the system is shown in Figure7.Figure 7
System framework.The design of this system aims to guide learners to learn the pronunciation of Russian words. It belongs to isolated word analysis in speech analysis technology. Compared with the analysis of the whole sentence, isolated word analysis technology is more mature, which increases the feasibility of the system. During the development of the system, full consideration should be given to the subsequent expansion of online translation function. To achieve this, it is necessary to make the system an open system, comply with certain development specifications, and simply add and reduce the system modules and configure the system hardware. Upgrade and update the system through the increase, decrease and modification of software modules.This system can further realize the online translation function by establishing a Chinese-English parallel corpus. The most common use of an entity relationship diagram is as a communication tool and bridge between database designers and database users during the analysis stage of database design. Entity relationship diagrams are used to create a conceptual data model, which is a representation of database structure that has nothing to do with database management systems or data models. This system is made up of three main components. After providing the tools for corpus construction, the crawler entity, message entity, and parallel webpage entity must develop data collection rules to ensure that the corpus and vocabulary pairs are useful and that the collected data meets specific requirements. Not only can corpus be used for linguistics research, but also it can be used to aid translation software. What kind of corpus pairs and vocabulary pairs can be provided to meet the requirements of the auxiliary translation software, all of which need to be constantly explored in the process of practice. At present, the rules formulated are the general outline, and the corpus extracted according to these rules may be further processed in the future, so as to be better provided for linguistic research and auxiliary translation tools. In the process of corpus collection and selection, on the one hand, linguists need to make unremitting efforts to widely collect corpus sources, investigate the standardization of translation and decide whether to be included in the database. On the other hand, it also needs technical support to make this process more high-quality and efficient. In the stage of corpus collection, we should pay attention to the proportion of different types of corpus, which meets the sampling standard; that is, the selection of a corpus should be comprehensive and representative.


## 5. Conclusions

With the continued advancement of economic globalization, the availability of information in various languages in various regions has greatly increased, and the demand for translation has risen to unprecedented heights. In terms of cost and efficiency, translation talent training cannot meet the demand for translation volume. The method used in this paper is a data-driven Russian phonetic conversion method. This method uses the WFST framework to achieve “many to many” alignment of Russian morphemes and phonemes using the expectation maximization algorithm and then combines the N-gram model to create the pronunciation model, which is then decoded using the WFST shortest path algorithm. This paper proposes a complete process and the main algorithms for building a multilingual parallel corpus for the translation industry in order to make full use of translation companies’ resources and improve the informatization level and work efficiency of the translation industry. Determining the objectives and principles of database construction, collecting and screening corpora, marking and processing corpora, inputting references, compiling explanatory materials, developing a query system, and so on are all part of the Russian-Chinese parallel corpus construction method. This also shows that the Chinese-Russian parallel corpus needs to be expanded in time, and some problems need to be further discussed. The corpus platform can present the original text, example translation, and student translation at the same time. Students can judge the possible problems in the translation by observing abnormal values. In the era of big data, with the continuous improvement of data use in quantity, speed, and diversity, parallel corpus-related data will provide broader expansion space and stronger support for translation and translation teaching. This work is an effective combination of speech analysis technology and Russian teaching and has strong reference significance for the further combination and development of speech technology and Russian teaching.



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*Source: 8023115-2022-03-23.xml*