Abstract
This paper describes the development of a program for analysis of intoning of verbal pieces in the Russian language. The goal is to measure the differences between the intoning of verbal pieces by both native and international Russian language speakers. The research methodology is based on the application of neural network analysis for solving the task of identification of speech samples, obtained by recording inophones’ speech. The experiment was carried out with the participation of 12 people: native speakers of the Russian language and the Chinese language, both male and female, aged from 20 to 35. A total number of speech samples amounted to 4800 items. Overall, 10 speech items in declarative and interrogative intonation were analyzed. A neural network that provides an assessment of correspondence of a speech sample to the standard variant of intoning was formed and trained. The results of experimental research are presented in the form of statistical assessments of pronouncing the verbal pieces with various intonations. These results are recommended to be applied in the process of learning Russian as a foreign language: the obtained data are considered as the confidence threshold of intoning identification, which complies with the standard or deviates from it. The results can also be applied for the individualized automated compilation of recommendations on correction of mistakes.
Highlights
Modern linguistics and lingoudidactics set out a number of tasks to researchers and methodologists: communicative and pragmatic approach in teaching, individualization and national orientation of teaching, optimization of balance between classroom and extracurricular work of students
Improving the efficiency of the cepstral analysis method can be achieved by optimizing the parameters of the cepstral vector by the criterion of minimum cross-connections between speech samples [2]
In order to develop such a system and increase the use of dialogues in the process of teaching a language, it is necessary to measure the differences between the intoning of individual speech segments by the Russian language native speakers and by the Chinese language native speakers studying the Russian language; determine the influence of the intonation system of the native language on the studied language and systemize the pronouncing variants interpreted as close as possible to the standard invariant of pronunciation
Summary
The speech sample x(t) is formed by a recording device G. In block F the speech sample is transformed into a frequency domain, which in the form of X(jω) enters block K of M(ω) the composite cepstral vector formation. For each speech sample a composite cepstral vector of m dimension is formed of successively connected cepstral vectors calculated for a single frame, into which a speech sample is divided. A number of parameters of a cepstral vector of a single frame can be selected by the criterion of the maximum correlation of an intonation image which is formed in it together with all speech samples which make up their general totality. Any speech samples are limited in duration of their pronunciation This makes it possible to perform their normalization by a number of nt time intervals, on which they will be divided before conversion to a frequency domain. M(ω) the composite cepstral vector enters N feedforward neural network, the diagram of which is shown on Fig.
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