Today’s human-computer interaction systems have a broad variety of applications in which automatic human emotion recognition is of great interest. Literature contains many different, more or less successful forms of these systems. This work emerged as an attempt to clarify which speech features are the most informative, which classification structure is the most convenient for this type of tasks, and the degree to which the results are influenced by database size, quality and cultural characteristic of a language. The research is presented as the case study on Slavic languages.

The article describes the problem of selection of heat treatment parameters to obtain the required mechanical properties in heat- treated

bronzes. A methodology for the construction of a classification model based on rough set theory is presented. A model of this type allows

the construction of inference rules also in the case when our knowledge of the existing phenomena is incomplete, and this is situation

commonly encountered when new materials enter the market. In the case of new test materials, such as the grade of bronze described in

this article, we still lack full knowledge and the choice of heat treatment parameters is based on a fragmentary knowledge resulting from

experimental studies. The measurement results can be useful in building of a model, this model, however, cannot be deterministic, but can

only approximate the stochastic nature of phenomena. The use of rough set theory allows for efficient inference also in areas that are not

yet fully explored.