Nizhniy Novgorod, Russia

Linguistic University of Nizhny Novgorod
Nizhniy Novgorod, Russia
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Savchenko V.V.,Linguistic University of Nizhny Novgorod
Radiophysics and Quantum Electronics | Year: 2017

Using the theoretic-information approach and the criterion of the information-divergence minimum in the Kullback–Leibler metric, we propose a new algorithm for checking the time series for stationarity in a broad sense. We consider an example of realizing this algorithm, study its dynamic characteristics, and give recommendations on its use under conditions of small samples. © 2017 Springer Science+Business Media, LLC

Savchenko V.V.,Linguistic University of Nizhny Novgorod
Journal of Communications Technology and Electronics | Year: 2017

A new algorithm for the suppression of background noise in systems of recognition of voice commands is proposed based on phonetic decoding of words. The principle of the algorithm employs frequencydomain correction of quasi-stationary fragments of the signal of voice command using selective estimation of noise variance in pauses between syllables. The desired effect lies in a several-fold increase in the signal-to-noise ratio at the input of the decision unit. The theoretical results are proven with the aid of a physical experiment. © 2017, Pleiades Publishing, Inc.

Zhigalev B.A.,Linguistic University of Nizhny Novgorod | Vikulina V.A.,Linguistic University of Nizhny Novgorod | Bezukladnikov K.E.,Perm State University
Life Science Journal | Year: 2014

The article highlights the problem of testing as one of the most actual aspects of training quality control management and assessment means in pedagogical diagnostics. The theoretical principles of testing, its development and application at education process and approaches to testing in Russia's and foreign education paradigms are touched upon. The authors also concentrate on investigating its complementary structural components and functions. This research allows classifying tests applied in education into a number of categories.

Nemova O.A.,Nizhny Novgorod State Pedagogical University | Retivina V.V.,Linguistic University of Nizhny Novgorod | Kutepova L.I.,Nizhny Novgorod State Pedagogical University | Vinnikova I.S.,Nizhny Novgorod State Pedagogical University | Kuznetsova E.A.,Nizhny Novgorod State Pedagogical University
International Journal of Environmental and Science Education | Year: 2016

The paper considers the issue of functioning of the mechanism of formation and translation of values of labor in family. Fundamental labor values and main channels of their distribution are revealed based on empiric material. Family influence on motivation of today’s Russian youth’s labor behavior was determined. An intergenerational comparative analysis of labor mindset and values of parent’s generation and their children was carried out. Random sampling was used for designing “parent” sampling: parents of students of two state universities of Nizhny Novgorod, Minin University and Dobrolyubov University, were interviewed. Survey type-hands-on: students were supposed to interview parents with respect to their labor mindset. Respondent parents were asked a question on values they consider important for their children. It was proposed to choose at most three from 7 options: prestigious work, high income, self-fulfillment opportunities, interesting work, socially useful labor, family well-being, health. Family well-being turned out to be the most significant for respondents. 79,5% of respondents chose this answer. Health ranks second-76,2%. 36,9% of respondents distinguished the role of interesting work. High income ranks fourth (36,1%), 32,0% and 25,4% of parents wish their children self-fulfillment opportunities and prestigious work, correspondingly. Socially useful labor appeared least important among suggested options, only 3,3% of respondents checked it. The percentage of parents, who highlighted their own influence on children’s occupational choice and support of that choice, is high. The research also demonstrated that today’s students are much less involved in household work than parents at their age. © 2016 Nemova et al.

Savchenko A.V.,National Research University Higher School of Economics | Savchenko L.V.,Linguistic University of Nizhny Novgorod
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

The problem of recognition of a sequence of objects (e.g., video-based image recognition, phoneme recognition) is explored. The generalization of the fuzzy phonetic decoding method is proposed by assuming the distribution of the classified object to be of exponential type. Its preliminary phase includes association of each model object with the fuzzy set of model classes with grades of membership defined as the confusion probabilities estimated with the Kullback-Leibler divergence between model distributions. At first, each object (e.g., frame) in a classified sequence is put in correspondence with the fuzzy set which grades are defined as the posterior probabilities. Next, this fuzzy set is intersected with the fuzzy set corresponding to the nearest neighbor. Finally, the arithmetic mean of these fuzzy intersections is assigned to the decision for the whole sequence. In this paper we propose not to limit the method's usage with the Kullback-Leibler discrimination and to estimate the grades of membership of models and query objects based on an arbitrary distance with appropriate scale factor. The experimental results in the problem of isolated Russian vowel phonemes and words recognition for state-of-the-art measures of similarity are presented. It is shown that the correct choice of the scale parameter can significantly increase the recognition accuracy. © 2014 Springer International Publishing.

Savchenko V.V.,Linguistic University of Nizhny Novgorod | Savchenko A.V.,National Research University Higher School of Economics
Journal of Communications Technology and Electronics | Year: 2016

A words phonetic decoding method in automatic speech recognition is considered. The properties of Kullback–Leibler divergence are used to synthesize the estimation of the distribution of divergence between minimum speech units (e.g., single phonemes) inside a single class. It is demonstrated that the minimum variance of the intraphonemic divergence is reached when the phonetic database is tuned to the voice of a single speaker. The estimations are proven by experimental results on the recognition of vowel sounds and isolated words of Russian language. © 2016, Pleiades Publishing, Inc.

Savchenko A.V.,National Research University Higher School of Economics | Savchenko L.V.,Linguistic University of Nizhny Novgorod
Pattern Recognition Letters | Year: 2015

The key purpose of this paper is to train a voice control system if a small amount of user speech data is available without need for general acoustic model if the latter does not fit to the user voice due to known variability sources (childhood, voice diseases, non-nativeness, etc.). We explore the possibility to increase the recognition rate by requiring the speaker to put the stress on all vowels in a command. We propose the novel modification of our fuzzy phonetic decoding method, in which each vowel is put in correspondence with a fuzzy union of sets of available reference signals from this class. A first, syllables are detected and phoneme segmentation is performed. Secondly, the command is extracted from spontaneous speech by thresholding the ratio of the duration of homogeneous segments to the duration of the whole syllable. Finally, each syllable is put in correspondence with the fuzzy set of vowels, and commands are ordered based on similarity with the fuzzy set of the utterance. The experimental results in synthetic and real Russian datasets prove that our method is characterized by better accuracy in comparison with known recognition methods. © 2015 Elsevier B.V.

Savchenko V.V.,Linguistic University of Nizhny Novgorod
Radiophysics and Quantum Electronics | Year: 2015

We consider the issue of small observation samples in the problem of spectral analysis of the random time series. It is proposed to solve the considered problem using the information-theoretic approach and a new algorithm based on the principle of minimum divergence of the cognominal spectral estimates yielded by the results of several independent observations in the Kullback–Leibler information metric. An example of a practical realization of the algorithm is considered and its asymptotic properties are studied. © 2015, Springer Science+Business Media New York.

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