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The paper presents recent results obtained within the ongoing project dedicated to the study of Russian pragmatic markers. Pragmatic markers are obligatory elements of natural speech in any language; moreover, they are considered to be functionally important for speech production and overcoming inevitable speech difficulties. A correct understanding of use and functions of pragmatic markers is a prerequisite for solution of many applied tasks related to speech technologies. The research is carried out on the data of two speech corpora — ORD corpus of Russian Everyday Speech known as “One Day of Speech” corpus and SAT corpus “Balanced Annotated Collection of Texts”, which consists primarily of monologues. The article describes the database of Russian pragmatic markers designed to support both linguistic and pragmatic studies of spoken Russian and the development of speech technologies for everyday discourse. Besides, it presents actual statistical data on pragmatic markers distribution in natural speech depending on different factors.

Pragmatic markers (PMs) mainly have an influence on a pragmatic aspect of communication and are mostly devoid of their own referential meaning. These markers are indispensable elements of oral communication in any language. The article suggests a typology of pragmatic markers for Russian everyday speech that includes 10 basic types. The frequency study for the use of various marker types is carried out on the basis of two representative speech corpora – a corpus of Russian Everyday Speech “One Speech Day” (ORD) and “Balanced Annotated Collection of Texts” (SAT). Preliminary data about PM distribution in dialogues and monologues was obtained and the article describes the main difficulties one comes across while annotating PMs according to our methodology. The main requirements for creating a Dictionary of Pragmatic Markers are enumerated. The paper indicates the scope of pragmatic markers and further prospects for their use, which includes (but not limited to) datasets labelling for voice assistants and speech recognition systems development.

In this paper we investigate how reliable wireless networks are if their coverage is incomplete and their subscribers move. We give a review of approaches to compute the reliability of such networks. Also, we proposed our own method to calculate the reliability of a communication network; the method uses a probability graph as a network model. The transition probability between the graph nodes depends on the distance between the network transmitters and the subscribers; the probability varies over time. We give an example of how to compute reliability.