Control Systems and Computers, N2, 2017, Article 3


Upr. sist. maš., 2017, Issue 2 (268), pp. 38-45.

UDC 004.934

Sazhok Mykola M., Ph.D. (Eng.), International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine (IRTC IT and S), Ukraine, Kiev, E-mail:

Speech Information Technologies and Systems

Introduction. Speech technology and systems became the part of the contemporary world, which helps to transform the society. The Generative Model proposed in Ukraine in 1960s became a base of modern and most productive techniques for speech recognition as well as for pattern recognition in general.

The purpose is to analyze and generalize the theoretical and applied progress in order to characterize the state-of-the-art, shape trends and suggest further research and development.

Scope of application. Speech technology scope extends continuously by introducing new speech signal sources and multilingualism, growing user expectations and IT progress. Voice input and text-to-speech systems allow relieving human hands and eyes that leads to the natural man-machine communication where a user can co-operate with the cybernetic system. An opposite example, where no such co-operation provided, is the automatic broadcast monitoring system.

Methods. The contemporary formulation of Generative Model is presented with the focus on its acoustic component. The recently adapted mathematical tools, which allow the context dependency and pattern hierarchy effective modelling, are referred. For decades, the feature space areas, where a basic speech segment is observed, have been successfully approximated by Gaussian Mixture Model. The further applied Deep Learning technique improves the approximation quality. The way to model the multilingual aspects are described. The general model of the speech recognition system is presented and the key applications are described.

Conclusion. The gap between computer and human speech processing has been significantly reduced for the certain tasks. The extended (e.g. structural) context dependency and feature space modelling showed their effectiveness and is promising for the further research.

Keywords: speech signal recognition, speech understanding, text-to-speech, spoken dialog systems, generative model.

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Received 30.04.2017