  Text Normalization is an integral part of any text-to-speech synthesis
system. In a natural language text, there are elements such as numbers, dates,
abbreviations, etc. that belong to other semiotic classes. They are called
non-standard words (NSW) and need to be expanded into ordinary words. For this
purpose, it is necessary to identify the semiotic class of each NSW. The
taxonomy of semiotic classes adapted to the Lithuanian language is presented in
the work. Sets of rules are created for detecting and expanding NSWs based on
regular expressions. Experiments with three completely different data sets were
performed and the accuracy was assessed. Causes of errors are explained and
recommendations are given for the development of text normalization rules.
